Recommender system - Knowledge (XXG)

940:

domain of citation recommender systems, users typically do not rate a citation or recommended article. In such cases, offline evaluations may use implicit measures of effectiveness. For instance, it may be assumed that a recommender system is effective that is able to recommend as many articles as possible that are contained in a research article's reference list. However, this kind of offline evaluations is seen critical by many researchers. For instance, it has been shown that results of offline evaluations have low correlation with results from user studies or A/B tests. A dataset popular for offline evaluation has been shown to contain duplicate data and thus to lead to wrong conclusions in the evaluation of algorithms. Often, results of so-called offline evaluations do not correlate with actually assessed user-satisfaction. This is probably because offline training is highly biased toward the highly reachable items, and offline testing data is highly influenced by the outputs of the online recommendation module. Researchers have concluded that the results of offline evaluations should be viewed critically.

1135:(AI) applications in recommendation systems are the advanced methodologies that leverage AI technologies, to enhance the performance recommendation engines. The AI-based recommender can analyze complex data sets, learning from user behavior, preferences, and interactions to generate highly accurate and personalized content or product suggestions. The integration of AI in recommendation systems has marked a significant evolution from traditional recommendation methods. Traditional methods often relied on inflexible algorithms that could suggest items based on general user trends or apparent similarities in content. In comparison, AI-powered systems have the capability to detect patterns and subtle distinctions that may be overlooked by traditional methods. These systems can adapt to specific individual preferences, thereby offering recommendations that are more aligned with individual user needs. This approach marks a shift towards more personalized, user-centric suggestions. 677:, content-based filtering, and other approaches. There is no reason why several different techniques of the same type could not be hybridized. Hybrid approaches can be implemented in several ways: by making content-based and collaborative-based predictions separately and then combining them; by adding content-based capabilities to a collaborative-based approach (and vice versa); or by unifying the approaches into one model. Several studies that empirically compared the performance of the hybrid with the pure collaborative and content-based methods and demonstrated that the hybrid methods can provide more accurate recommendations than pure approaches. These methods can also be used to overcome some of the common problems in recommender systems such as cold start and the sparsity problem, as well as the knowledge engineering bottleneck in 1057:, et al. criticized that "it is currently difficult to reproduce and extend recommender systems research results," and that evaluations are "not handled consistently". Konstan and Adomavicius conclude that "the Recommender Systems research community is facing a crisis where a significant number of papers present results that contribute little to collective knowledge often because the research lacks the evaluation to be properly judged and, hence, to provide meaningful contributions." As a consequence, much research about recommender systems can be considered as not reproducible. Hence, operators of recommender systems find little guidance in the current research for answering the question, which recommendation approaches to use in a recommender systems. 441:. Collaborative filtering is based on the assumption that people who agreed in the past will agree in the future, and that they will like similar kinds of items as they liked in the past. The system generates recommendations using only information about rating profiles for different users or items. By locating peer users/items with a rating history similar to the current user or item, they generate recommendations using this neighborhood. Collaborative filtering methods are classified as memory-based and model-based. A well-known example of memory-based approaches is the user-based algorithm, while that of model-based approaches is 1045:, IJCAI), has shown that on average less than 40% of articles could be reproduced by the authors of the survey, with as little as 14% in some conferences. The articles considers a number of potential problems in today's research scholarship and suggests improved scientific practices in that area. More recent work on benchmarking a set of the same methods came to qualitatively very different results whereby neural methods were found to be among the best performing methods. Deep learning and neural methods for recommender systems have been used in the winning solutions in several recent recommender system challenges, WSDM, 1066:

recommendation algorithms or scenarios led to strong changes in the effectiveness of a recommender system. They conclude that seven actions are necessary to improve the current situation: "(1) survey other research fields and learn from them, (2) find a common understanding of reproducibility, (3) identify and understand the determinants that affect reproducibility, (4) conduct more comprehensive experiments (5) modernize publication practices, (6) foster the development and use of recommendation frameworks, and (7) establish best-practice guidelines for recommender-systems research."

1161:(CF) is one of the most commonly used recommendation system algorithms. It generates personalized suggestions for users based on explicit or implicit behavioral patterns to form predictions. Specifically, it relies on external feedback such as star ratings, purchasing history and so on to make judgments. CF make predictions about users' preference based on similarity measurements. Essentially, the underlying theory is: "if user A is similar to user B, and if A likes item C, then it is likely that B also likes item C." 829:. From 2006 to 2009, Netflix sponsored a competition, offering a grand prize of $ 1,000,000 to the team that could take an offered dataset of over 100 million movie ratings and return recommendations that were 10% more accurate than those offered by the company's existing recommender system. This competition energized the search for new and more accurate algorithms. On 21 September 2009, the grand prize of US$ 1,000,000 was given to the BellKor's Pragmatic Chaos team using tiebreaking rules. 318:. Collaborative filtering approaches build a model from a user's past behavior (items previously purchased or selected and/or numerical ratings given to those items) as well as similar decisions made by other users. This model is then used to predict items (or ratings for items) that the user may have an interest in. Content-based filtering approaches utilize a series of discrete, pre-tagged characteristics of an item in order to recommend additional items with similar properties. 1210:(ANN), is a deep learning model structure which aims to mimic a human brain. They comprise a series of neurons, each responsible for receiving and processing information transmitted from other interconnected neurons. Similar to a human brain, these neurons will change activation state based on incoming signals (training input and backpropagated output), allowing the system to adjust activation weights during the network learning phase. ANN is usually designed to be a 750:

learning that is of particular use in the area of recommender systems is the fact that the models or policies can be learned by providing a reward to the recommendation agent. This is in contrast to traditional learning techniques which rely on supervised learning approaches that are less flexible, reinforcement learning recommendation techniques allow to potentially train models that can be optimized directly on metrics of engagement, and user interest.

737:

current user session. Domains, where session-based recommendations are particularly relevant, include video, e-commerce, travel, music and more. Most instances of session-based recommender systems rely on the sequence of recent interactions within a session without requiring any additional details (historical, demographic) of the user. Techniques for session-based recommendations are mainly based on generative sequential models such as

636:

recommendation system is significantly less than when other content types from other services can be recommended. For example, recommending news articles based on news browsing is useful. Still, it would be much more useful when music, videos, products, discussions, etc., from different services, can be recommended based on news browsing. To overcome this, most content-based recommender systems now use some form of the hybrid system.

430: 576:. Content-based filtering methods are based on a description of the item and a profile of the user's preferences. These methods are best suited to situations where there is known data on an item (name, location, description, etc.), but not on the user. Content-based recommenders treat recommendation as a user-specific classification problem and learn a classifier for the user's likes and dislikes based on an item's features. 1080: 1345:. Therefore, there is a risk that the market could become fragmented, leaving it to the viewer to visit various locations and find what they want to watch in a way that is time-consuming and complicated for them. By using a search and recommendation engine, viewers are provided with a central 'portal' from which to discover content from several sources in just one location. 759:

predict a rating for unexplored items of u by exploiting preference information on multiple criteria that affect this overall preference value. Several researchers approach MCRS as a multi-criteria decision making (MCDM) problem, and apply MCDM methods and techniques to implement MCRS systems. See this chapter for an extended introduction.

2845: 768:

pushing recommendations in certain circumstances, for instance, during a professional meeting, early morning, or late at night. Therefore, the performance of the recommender system depends in part on the degree to which it has incorporated the risk into the recommendation process. One option to manage this issue is

1150:. These advanced methods enhance system capabilities to predict user preferences and deliver personalized content more accurately. Each technique contributes uniquely. The following sections will introduce specific AI models utilized by a recommendation system by illustrating their theories and functionalities. 1247:

Natural language processing is a series of AI algorithms to make natural human language accessible and analyzable to a machine. It is a fairly modern technique inspired by the growing amount of textual information. For application in recommendation system, a common case is the Amazon customer review.

963:– In some situations, it is more effective to re-show recommendations, or let users re-rate items, than showing new items. There are several reasons for this. Users may ignore items when they are shown for the first time, for instance, because they had no time to inspect the recommendations carefully. 855:

A number of privacy issues arose around the dataset offered by Netflix for the Netflix Prize competition. Although the data sets were anonymized in order to preserve customer privacy, in 2007 two researchers from the University of Texas were able to identify individual users by matching the data sets

687:

is a good example of the use of hybrid recommender systems. The website makes recommendations by comparing the watching and searching habits of similar users (i.e., collaborative filtering) as well as by offering movies that share characteristics with films that a user has rated highly (content-based

301:

articles to television. As operators compete to be the gateway to home entertainment, personalized television is a key service differentiator. Academic content discovery has recently become another area of interest, with several companies being established to help academic researchers keep up to date

1010:

is a measure of "how surprising the recommendations are". For instance, a recommender system that recommends milk to a customer in a grocery store might be perfectly accurate, but it is not a good recommendation because it is an obvious item for the customer to buy. " serves two purposes: First, the

916:

Evaluating the performance of a recommendation algorithm on a fixed test dataset will always be extremely challenging as it is impossible to accurately predict the reactions of real users to the recommendations. Hence any metric that computes the effectiveness of an algorithm in offline data will be

448:

A key advantage of the collaborative filtering approach is that it does not rely on machine analyzable content and therefore it is capable of accurately recommending complex items such as movies without requiring an "understanding" of the item itself. Many algorithms have been used in measuring user

341:) to seed a "station" that plays music with similar properties. User feedback is used to refine the station's results, deemphasizing certain attributes when a user "dislikes" a particular song and emphasizing other attributes when a user "likes" a song. This is an example of a content-based approach. 1040:

in recommender systems publications. The topic of reproducibility seems to be a recurrent issue in some Machine Learning publication venues, but does not have a considerable effect beyond the world of scientific publication. In the context of recommender systems a 2019 paper surveyed a small number

939:

The effectiveness of recommendation approaches is then measured based on how well a recommendation approach can predict the users' ratings in the dataset. While a rating is an explicit expression of whether a user liked a movie, such information is not available in all domains. For instance, in the

583:

is built to indicate the type of item this user likes. In other words, these algorithms try to recommend items similar to those that a user liked in the past or is examining in the present. It does not rely on a user sign-in mechanism to generate this often temporary profile. In particular, various

371:

created the first recommender system in 1979, called Grundy. She looked for a way to recommend users books they might like. Her idea was to create a system that asks users specific questions and classifies them into classes of preferences, or "stereotypes", depending on their answers. Depending on

261:

generators for video and music services, product recommenders for online stores, or content recommenders for social media platforms and open web content recommenders. These systems can operate using a single type of input, like music, or multiple inputs within and across platforms like news, books

794:

to offer personalized, context-sensitive recommendations. This is a particularly difficult area of research as mobile data is more complex than data that recommender systems often have to deal with. It is heterogeneous, noisy, requires spatial and temporal auto-correlation, and has validation and

767:

The majority of existing approaches to recommender systems focus on recommending the most relevant content to users using contextual information, yet do not take into account the risk of disturbing the user with unwanted notifications. It is important to consider the risk of upsetting the user by

749:

The recommendation problem can be seen as a special instance of a reinforcement learning problem whereby the user is the environment upon which the agent, the recommendation system acts upon in order to receive a reward, for instance, a click or engagement by the user. One aspect of reinforcement

1218:

ANN is widely used in recommendation systems for its power to utilize various data. Other than feedback data, ANN can incorporate non-feedback data which are too intricate for collaborative filtering to learn, and the unique structure allows ANN to identify extra signal from non-feedback data to

810:

to generate driving routes for taxi drivers in a city. This system uses GPS data of the routes that taxi drivers take while working, which includes location (latitude and longitude), time stamps, and operational status (with or without passengers). It uses this data to recommend a list of pickup

758:

Multi-criteria recommender systems (MCRS) can be defined as recommender systems that incorporate preference information upon multiple criteria. Instead of developing recommendation techniques based on a single criterion value, the overall preference of user u for the item i, these systems try to

635:

A key issue with content-based filtering is whether the system can learn user preferences from users' actions regarding one content source and use them across other content types. When the system is limited to recommending content of the same type as the user is already using, the value from the

333:

Last.fm creates a "station" of recommended songs by observing what bands and individual tracks the user has listened to on a regular basis and comparing those against the listening behavior of other users. Last.fm will play tracks that do not appear in the user's library, but are often played by

1273:

An emerging market for content discovery platforms is academic content. Approximately 6000 academic journal articles are published daily, making it increasingly difficult for researchers to balance time management with staying up to date with relevant research. Though traditional tools academic

985:

is particularly notable for the detailed personal information released in its dataset. Ramakrishnan et al. have conducted an extensive overview of the trade-offs between personalization and privacy and found that the combination of weak ties (an unexpected connection that provides serendipitous

798:

There are three factors that could affect the mobile recommender systems and the accuracy of prediction results: the context, the recommendation method and privacy. Additionally, mobile recommender systems suffer from a transplantation problem – recommendations may not apply in all regions (for

644:

of items, because as they also reflect aspects of the item like metadata, extracted features are widely concerned by the users. Sentiments extracted from the reviews can be seen as users' rating scores on the corresponding features. Popular approaches of opinion-based recommender system utilize

615:

representation (also called vector space representation). The system creates a content-based profile of users based on a weighted vector of item features. The weights denote the importance of each feature to the user and can be computed from individually rated content vectors using a variety of

736:

These recommender systems use the interactions of a user within a session to generate recommendations. Session-based recommender systems are used at YouTube and Amazon. These are particularly useful when history (such as past clicks, purchases) of a user is not available or not relevant in the

639:

Content-based recommender systems can also include opinion-based recommender systems. In some cases, users are allowed to leave text reviews or feedback on the items. These user-generated texts are implicit data for the recommender system because they are potentially rich resources of both

924:

In A/B tests, recommendations are shown to typically thousands of users of a real product, and the recommender system randomly picks at least two different recommendation approaches to generate recommendations. The effectiveness is measured with implicit measures of effectiveness such as

1065:

conducted a study of papers published in the field, as well as benchmarked some of the most popular frameworks for recommendation and found large inconsistencies in results, even when the same algorithms and data sets were used. Some researchers demonstrated that minor variations in the

3828:

Jannach, Dietmar; Lerche, Lukas; Gedikli, Fatih; Bonnin, Geoffray (June 10, 2013). "What Recommenders Recommend – an Analysis of Accuracy, Popularity, and Sales Diversity Effects". In Carberry, Sandra; Weibelzahl, Stephan; Micarelli, Alessandro; Semeraro, Giovanni (eds.).

1290:

that takes a researchers' authorized paper and citations as input. Whilst these recommendations have been noted to be extremely good, this poses a problem with early career researchers which may be lacking a sufficient body of work to produce accurate recommendations.

610:

Basically, these methods use an item profile (i.e., a set of discrete attributes and features) characterizing the item within the system. To abstract the features of the items in the system, an item presentation algorithm is applied. A widely used algorithm is the

2842: 249:

that provides suggestions for items that are most pertinent to a particular user. Recommender systems are particularly useful when an individual needs to choose an item from a potentially overwhelming number of items that a service may offer.

563:

originally used collaborative filtering to recommend new friends, groups, and other social connections by examining the network of connections between a user and their friends. Collaborative filtering is still used as part of hybrid systems.

1282:

provide a readily accessible database of journal articles, content recommendation in these cases are performed in a 'linear' fashion, with users setting 'alarms' for new publications based on keywords, journals or particular authors.

349:

problem, and is common in collaborative filtering systems. Whereas Pandora needs very little information to start, it is far more limited in scope (for example, it can only make recommendations that are similar to the original seed).

3056:, pp. 377-408, The Adaptive Web, Peter Brusilovsky, Alfred Kobsa, Wolfgang Nejdl (Ed.), Lecture Notes in Computer Science, Springer-Verlag, Berlin, Germany, Lecture Notes in Computer Science, Vol. 4321, May 2007, 978-3-540-72078-2. 3942:

Langer, Stefan (September 14, 2015). "A Comparison of Offline Evaluations, Online Evaluations, and User Studies in the Context of Research-Paper Recommender Systems". In Kapidakis, Sarantos; Mazurek, Cezary; Werla, Marcin (eds.).

1214:

model. Unlike regular machine learning where the underlying theoretical components are formal and rigid, the collaborative effects of neurons are not entirely clear, but modern experiments has shown the predictive power of ANN.

2494: 4009:

Beel, Joeran; Genzmehr, Marcel; Langer, Stefan; Nürnberger, Andreas; Gipp, Bela (January 1, 2013). "A comparative analysis of offline and online evaluations and discussion of research paper recommender system evaluation".

912:

are useful to assess the quality of a recommendation method. Diversity, novelty, and coverage are also considered as important aspects in evaluation. However, many of the classic evaluation measures are highly criticized.

1027:(CTR) for recommendations labeled as "Sponsored" were lower (CTR=5.93%) than CTR for identical recommendations labeled as "Organic" (CTR=8.86%). Recommendations with no label performed best (CTR=9.87%) in that study. 1017:– A recommender system is of little value for a user if the user does not trust the system. Trust can be built by a recommender system by explaining how it generates recommendations, and why it recommends an item. 1248:

Amazon will analyze the feedbacks comments from each customer and report relevant data to other customers for reference. The recent years have witnessed the development of various text analysis models, including

994:– Beel et al. found that user demographics may influence how satisfied users are with recommendations. In their paper they show that elderly users tend to be more interested in recommendations than younger users. 548:: The number of items sold on major e-commerce sites is extremely large. The most active users will only have rated a small subset of the overall database. Thus, even the most popular items have very few ratings. 920:

User studies are rather a small scale. A few dozens or hundreds of users are presented recommendations created by different recommendation approaches, and then the users judge which recommendations are best.

542:: There are millions of users and products in many of the environments in which these systems make recommendations. Thus, a large amount of computation power is often necessary to calculate recommendations. 5290: 3049: 345:

Each type of system has its strengths and weaknesses. In the above example, Last.fm requires a large amount of information about a user to make accurate recommendations. This is an example of the

844:

Many benefits accrued to the web due to the Netflix project. Some teams have taken their technology and applied it to other markets. Some members from the team that finished second place founded

257:, such as what product to purchase, what music to listen to, or what online news to read. Recommender systems are used in a variety of areas, with commonly recognised examples taking the form of 2336: 2531: 1049:. Moreover, neural and deep learning methods are widely used in industry where they are extensively tested. The topic of reproducibility is not new in recommender systems. By 2011, 1305:

In contrast to an engagement-based ranking system employed by social media and other digital platforms, a bridging-based ranking optimizes for content that is unifying instead of

4351: 948:

Typically, research on recommender systems is concerned with finding the most accurate recommendation algorithms. However, there are a number of factors that are also important.

410:

et al. discussed the problems of offline evaluations. Beel et al. have also provided literature surveys on available research paper recommender systems and existing challenges.

357:

since they help users discover items they might not have found otherwise. Of note, recommender systems are often implemented using search engines indexing non-traditional data.

285:

in order to discover and recommend appropriate content, whilst reducing ongoing maintenance and development costs. A content discovery platform delivers personalized content to

832:

The most accurate algorithm in 2007 used an ensemble method of 107 different algorithmic approaches, blended into a single prediction. As stated by the winners, Bell et al.:

5401: 4202: 4070:. 41st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2018). Ann Arbor, Michigan, USA: ACM. pp. 415–424. 2003:

Bobadilla, J.; Ortega, F.; Hernando, A.; Alcalá, J. (2011). "Improving collaborative filtering recommender system results and performance using genetic algorithms".

2078: 1337:

As the connected television landscape continues to evolve, search and recommendation are seen as having an even more pivotal role in the discovery of content. With

860:. As a result, in December 2009, an anonymous Netflix user sued Netflix in Doe v. Netflix, alleging that Netflix had violated United States fair trade laws and the 4906:

Konstan, Joseph A.; Adomavicius, Gediminas (January 1, 2013). "Toward identification and adoption of best practices in algorithmic recommender systems research".

5294: 3095:

Chen, Minmin; Beutel, Alex; Covington, Paul; Jain, Sagar; Belletti, Francois; Chi, Ed (2018). "Top-K Off-Policy Correction for a REINFORCE Recommender System".

1011:

chance that users lose interest because the choice set is too uniform decreases. Second, these items are needed for algorithms to learn and improve themselves".

208: 552:

One of the most famous examples of collaborative filtering is item-to-item collaborative filtering (people who buy x also buy y), an algorithm popularized by

1928:

Chen, Hung-Hsuan; Chen, Pu (January 9, 2019). "Differentiating Regularization Weights -- A Simple Mechanism to Alleviate Cold Start in Recommender Systems".

4771: 3751:. In: Proceedings of the 33rd International ACMSIGIR Conference on Research and Development in Information Retrieval, SIGIR 2010, pp. 210–217. ACM, New York 3069:

Hidasi, Balázs; Karatzoglou, Alexandros; Baltrunas, Linas; Tikk, Domonkos (March 29, 2016). "Session-based Recommendations with Recurrent Neural Networks".

3046: 406:

provided a new, alternate overview of recommender systems. Herlocker provides an additional overview of evaluation techniques for recommender systems, and

3722: 2244: 1041:

of hand-picked publications applying deep learning or neural methods to the top-k recommendation problem, published in top conferences (SIGIR, KDD, WWW,

442: 105: 616:

techniques. Simple approaches use the average values of the rated item vector while other sophisticated methods use machine learning techniques such as

4680:

Rendle, Steffen; Krichene, Walid; Zhang, Li; Anderson, John (September 22, 2020). "Neural Collaborative Filtering vs. Matrix Factorization Revisited".

3793:

Chen, Hung-Hsuan; Chung, Chu-An; Huang, Hsin-Chien; Tsui, Wen (September 1, 2017). "Common Pitfalls in Training and Evaluating Recommender Systems".

5549: 532:: For a new user or item, there is not enough data to make accurate recommendations. Note: one commonly implemented solution to this problem is the 3479: 838:

Our experience is that most efforts should be concentrated in deriving substantially different approaches, rather than refining a single technique.

2222:

BEEL, Joeran, et al. Paper recommender systems: a literature survey. International Journal on Digital Libraries, 2016, 17. Jg., Nr. 4, S. 305–338.

584:

candidate items are compared with items previously rated by the user, and the best-matching items are recommended. This approach has its roots in

4524: 1175:: Create a n-dimensional space where each axis represents a user's trait (ratings, purchases, etc.). Represent the user as a point in that space. 5652: 4865:

Ekstrand, Michael D.; Ludwig, Michael; Konstan, Joseph A.; Riedl, John T. (January 1, 2011). "Rethinking the recommender research ecosystem".

3627: 3559:

Pimenidis, Elias; Polatidis, Nikolaos; Mouratidis, Haralambos (August 3, 2018). "Mobile recommender systems: Identifying the major concepts".

2557: 1500: 334:

other users with similar interests. As this approach leverages the behavior of users, it is an example of a collaborative filtering technique.

5559: 5534: 5063: 5022: 4923: 4882: 4828: 4746: 4709: 4649: 4326: 4185: 4037: 3993: 3960: 3918: 3864: 3409: 3338:

Xin, Xin; Karatzoglou, Alexandros; Arapakis, Ioannis; Jose, Joemon (2020). "Self-Supervised Reinforcement Learning for Recommender Systems".

3314: 3269: 3193: 3138: 2906: 2870: 2819: 2582: 2517: 2326:." In Proceedings of the SIGCHI conference on Human factors in computing systems, pp. 194–201. ACM Press/Addison-Wesley Publishing Co., 1995. 2306:." In Proceedings of the SIGCHI conference on Human factors in computing systems, pp. 210–217. ACM Press/Addison-Wesley Publishing Co., 1995. 1987: 1851: 1483: 1360: 1042: 849: 182: 5317: 5158:

Wu, L. (May 2023). "A Survey on Accuracy-Oriented Neural Recommendation: From Collaborative Filtering to Information-Rich Recommendation".

2986: 2282: 5416: 3680: 2442:

Herlocker, J. L.; Konstan, J. A.; Terveen, L. G.; Riedl, J. T. (January 2004). "Evaluating collaborative filtering recommender systems".

640:

feature/aspects of the item and users' evaluation/sentiment to the item. Features extracted from the user-generated reviews are improved

3537: 2085: 2075: 1097: 321:

The differences between collaborative and content-based filtering can be demonstrated by comparing two early music recommender systems,

1575:

Gupta, Pankaj; Goel, Ashish; Lin, Jimmy; Sharma, Aneesh; Wang, Dong; Zadeh, Reza (2013). "WTF: the who to follow service at Twitter".

955:– Users tend to be more satisfied with recommendations when there is a higher intra-list diversity, e.g. items from different artists. 2319: 5517: 5247: 2982:

The Deep Learning–Based Recommender System "Pubmender" for Choosing a Biomedical Publication Venue: Development and Validation Study

2780: 2097: 1726: 1694: 1592: 1119: 1023:– User satisfaction with recommendations may be influenced by the labeling of the recommendations. For instance, in the cited study 201: 100: 5524: 1197:: The system will analyze the similar preference of the k neighbors. The system will make recommendations based on that similarity 936:

Offline evaluations are based on historic data, e.g. a dataset that contains information about how users previously rated movies.

4770:

Schifferer, Benedikt; Deotte, Chris; Puget, Jean-François; de Souza Pereira, Gabriel; Titericz, Gilberto; Liu, Jiwei; Ak, Ronay.

4449:"Do not blame it on the algorithm: an empirical assessment of multiple recommender systems and their impact on content diversity" 1365: 346: 80: 3359:

Ie, Eugene; Jain, Vihan; Narvekar, Sanmit; Agarwal, Ritesh; Wu, Rui; Cheng, Heng-Tze; Chandra, Tushar; Boutilier, Craig (2019).

973:

using collaborative filtering can be problematic from a privacy point of view. Many European countries have a strong culture of

266:. Recommender systems have also been developed to explore research articles and experts, collaborators, and financial services. 978: 379:

at Columbia University, and implemented at scale and worked through in technical reports and publications from 1994 onwards by

1325:

planned to pilot in 2024. Aviv Ovadya also argues for implementing bridging-based algorithms in major platforms by empowering

1164:

There are many models available for collaborative filtering. For AI-applied collaborative filtering, a common model is called

969:– Recommender systems usually have to deal with privacy concerns because users have to reveal sensitive information. Building 722:: One recommendation technique is applied and produces some sort of model, which is then the input used by the next technique. 5642: 2303: 1101: 4296: 297:. A large range of content discovery platforms currently exist for various forms of content ranging from news articles and 3119:

Yifei, Ma; Narayanaswamy, Balakrishnan; Haibin, Lin; Hao, Ding (2020). "Temporal-Contextual Recommendation in Real-Time".

2264: 2174:

Herz, Frederick SM. "Customized electronic newspapers and advertisements." U.S. Patent 7,483,871, issued January 27, 2009.

1165: 981:

can result in a negative customer response. Much research has been conducted on ongoing privacy issues in this space. The

785: 658: 450: 2495:"A comparative analysis of offline and online evaluations and discussion of research paper recommender system evaluation" 1036:

Recommender systems are notoriously difficult to evaluate offline, with some researchers claiming that this has led to a

5647: 5637: 2956: 1395: 1253: 861: 246: 194: 1805: 716:: Recommenders are given strict priority, with the lower priority ones breaking ties in the scoring of the higher ones. 5442: 5376: 4852: 3889:

Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval

3763:

Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval

1257: 1147: 131: 5588: 4062: 2210: 2184: 1260:(LDA), etc. Their uses have consistently aimed to provide customers with more precise and tailored recommendations. 4146:

Ziegler CN, McNee SM, Konstan JA, Lausen G (2005). "Improving recommendation lists through topic diversification".

1355: 909: 629: 396: 136: 1610:"A capable multimedia content discovery platform based on visual content analysis and intelligent data enrichment" 1090: 799:

instance, it would be unwise to recommend a recipe in an area where all of the ingredients may not be available).

4782: 4232: 1207: 1191:: Based on the computed distances, find k nearest neighbors of the user to which we want to make recommendations 5087:

Khanal, S.S. (July 2020). "A systematic review: machine learning based recommendation systems for e-learning".

3726: 2241: 1405: 1341:-connected devices, consumers are projected to have access to content from linear broadcast sources as well as 1313:

and Remesh which have been used around the world to help find more consensus around specific political issues.

1249: 865: 738: 498: 461: 95: 90: 4908:

Proceedings of the International Workshop on Reproducibility and Replication in Recommender Systems Evaluation

4352:"The Impact of Demographics (Age and Gender) and Other User Characteristics on Evaluating Recommender Systems" 4012:

Proceedings of the International Workshop on Reproducibility and Replication in Recommender Systems Evaluation

3660: 2566:

Proceedings of the International Workshop on Reproducibility and Replication in Recommender Systems Evaluation

2502:

Proceedings of the International Workshop on Reproducibility and Replication in Recommender Systems Evaluation

4064:

Should I Follow the Crowd? A Probabilistic Analysis of the Effectiveness of Popularity in Recommender Systems

2394:"Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions" 314:

and content-based filtering (also known as the personality-based approach), as well as other systems such as

3433:

Lakiotaki, K.; Matsatsinis; Tsoukias, A (March 2011). "Multicriteria User Modeling in Recommender Systems".

2688:

Koren, Yehuda; Volinsky, Chris (August 1, 2009). "Matrix Factorization Techniques for Recommender Systems".

2349:

Montaner, M.; Lopez, B.; de la Rosa, J. L. (June 2003). "A Taxonomy of Recommender Agents on the Internet".

2339:." In Proceedings of the 1994 ACM conference on Computer supported cooperative work, pp. 175–186. ACM, 1994. 1828: 1375: 1370: 1158: 1132: 986:

recommendations) and other data sources can be used to uncover identities of users in an anonymized dataset.

901: 674: 438: 424: 375:

Another early recommender system, called a "digital bookshelf", was described in a 1990 technical report by

315: 311: 254: 141: 85: 49: 4727:"Are We Evaluating Rigorously? Benchmarking Recommendation for Reproducible Evaluation and Fair Comparison" 4510:

Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1

1565:

Resnick, Paul, and Hal R. Varian. "Recommender systems." Communications of the ACM 40, no. 3 (1997): 56–58.

1329:

that are representative of the platform's users to control the design and implementation of the algorithm.

403: 5048:

2020 2nd International Conference on Advances in Computing, Communication Control and Networking (ICACCCN)

4304: 4015: 3947:. Lecture Notes in Computer Science. Vol. 9316. Springer International Publishing. pp. 153–168. 3896: 3842: 3442: 2697: 2451: 2405: 1425: 1415: 1385: 1326: 1037: 617: 522: 110: 5565: 2163:

System and method for providing recommendation of goods and services based on recorded purchasing history

1719:

Proceedings of the 19th National Conference on Innovative Applications of Artificial Intelligence, vol. 2

4532:

Proceedings of the 17th International Conference on Theory and Practice of Digital Libraries (TPDL 2013)

4473: 4359:

Proceedings of the 17th International Conference on Theory and Practice of Digital Libraries (TPDL 2013)

4210:

Proceedings of the 17th International Conference on Theory and Practice of Digital Libraries (TPDL 2013)

3761:

Turpin, Andrew H; Hersh, William (2001). "Why batch and user evaluations do not give the same results".

3608: 3486: 1400: 1062: 650: 589: 585: 262:

and search queries. There are also popular recommender systems for specific topics like restaurants and

59: 4530:. In Trond Aalberg, Milena Dobreva, Christos Papatheodorou, Giannis Tsakonas, Charles Farrugia (eds.). 4357:. In Trond Aalberg; Milena Dobreva; Christos Papatheodorou; Giannis Tsakonas; Charles Farrugia (eds.). 4208:. In Trond Aalberg; Milena Dobreva; Christos Papatheodorou; Giannis Tsakonas; Charles Farrugia (eds.). 2124: 4381: 4203:"Persistence in Recommender Systems: Giving the Same Recommendations to the Same Users Multiple Times" 1050: 1046: 4448: 4429: 4262: 3696: 3365:

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI-19)

1182: 905: 338: 146: 4612:"Are we really making much progress? A worrying analysis of recent neural recommendation approaches" 4020: 3901: 3447: 3384:

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

3299:

Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

3121:

Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

3000: 2702: 2410: 881:

Evaluation is important in assessing the effectiveness of recommendation algorithms. To measure the

4772:"Using Deep Learning to Win the Booking.com WSDM WebTour21 Challenge on Sequential Recommendations" 3634: 2456: 1508: 1420: 1410: 1342: 1300: 974: 454: 402:

Montaner provided the first overview of recommender systems from an intelligent agent perspective.

5468: 5346: 4309: 3847: 2793:

Felício, Crícia Z.; Paixão, Klérisson V.R.; Barcelos, Celia A.Z.; Preux, Philippe (July 9, 2017).

2649:. In Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence (UAI'98). 5352: 5208: 5167: 5104: 5069: 5028: 4979: 4929: 4888: 4834: 4752: 4685: 4655: 4619: 4592: 4566: 4553:

Ferrari Dacrema, Maurizio; Boglio, Simone; Cremonesi, Paolo; Jannach, Dietmar (January 8, 2021).

4486: 4406: 4332: 4243: 4165: 4128: 4043: 3924: 3810: 3586: 3568: 3460: 3415: 3387: 3339: 3320: 3275: 3247: 3218: 3199: 3171: 3144: 3096: 3070: 2825: 2715: 2650: 2627: 2588: 2523: 2469: 2423: 2389: 2366: 2162: 2144: 1945: 1907: 1786: 1639: 1024: 930: 897: 773: 654: 533: 407: 278: 167: 4997:

Said, Alan; Bellogín, Alejandro (October 1, 2014). "Comparative recommender system evaluation".

4283:

Naren Ramakrishnan; Benjamin J. Keller; Batul J. Mirza; Ananth Y. Grama; George Karypis (2001).

3892: 3884: 2231:

RICH, Elaine. User modeling via stereotypes. Cognitive science, 1979, 3. Jg., Nr. 4, S. 329–354.

2089: 2069: 1463: 710:: Recommendations from different recommenders are presented together to give the recommendation. 337:

Pandora uses the properties of a song or artist (a subset of the 400 attributes provided by the

4300: 4284: 3748: 3677: 2898: 1058: 460:

When building a model from a user's behavior, a distinction is often made between explicit and

5614: 5555: 5530: 5513: 5395: 5243: 5059: 5018: 4971: 4919: 4878: 4824: 4742: 4705: 4645: 4478: 4322: 4181: 4033: 3989: 3956: 3914: 3860: 3838: 3523:

Yong Ge; Hui Xiong; Alexander Tuzhilin; Keli Xiao; Marco Gruteser; Michael J. Pazzani (2010).

3405: 3310: 3265: 3189: 3134: 2902: 2886: 2866: 2815: 2578: 2513: 2093: 1983: 1899: 1847: 1778: 1722: 1690: 1631: 1588: 1479: 1287: 1054: 904:, the latter having been used in the Netflix Prize. The information retrieval metrics such as 489: 115: 1232:: sequence of pages visited, time spent on different parts of a website, mouse movement, etc. 1000:– When users can participate in the recommender system, the issue of fraud must be addressed. 372:

users' stereotype membership, they would then get recommendations for books they might like.

5618: 5264: 5218: 5177: 5138: 5096: 5051: 5010: 5002: 4963: 4911: 4870: 4816: 4734: 4695: 4637: 4629: 4584: 4576: 4468: 4460: 4396: 4314: 4263:"Evaluating recommender systems from the user's perspective: survey of the state of the art" 4173: 4120: 4071: 4025: 3981: 3948: 3906: 3852: 3830: 3802: 3578: 3452: 3397: 3302: 3301:. KDD '18. London, United Kingdom: Association for Computing Machinery. pp. 1831–1839. 3257: 3238:

Li, Jing; Ren, Pengjie; Chen, Zhumin; Ren, Zhaochun; Lian, Tao; Ma, Jun (November 6, 2017).

3181: 3168:

Proceedings of the 27th ACM International Conference on Information and Knowledge Management

3124: 3026: 2935: 2890: 2807: 2707: 2619: 2570: 2505: 2461: 2415: 2358: 2136: 2082: 2046: 2012: 1975: 1937: 1891: 1839: 1768: 1682: 1621: 1580: 1543: 1471: 1139: 621: 354: 298: 5493: 4807:

Volkovs, Maksims; Rai, Himanshu; Cheng, Zhaoyue; Wu, Ga; Lu, Yichao; Sanner, Scott (2018).

3524: 3246:. CIKM '17. Singapore, Singapore: Association for Computing Machinery. pp. 1419–1428. 2798: 1679:

Proceedings of the 11th Annual International ACM/IEEE Joint Conference on Digital Libraries

1671: 3837:. Lecture Notes in Computer Science. Vol. 7899. Springer Berlin Heidelberg. pp. 3684: 3053: 2980:

X.Y. Feng, H. Zhang, Y.J. Ren, P.H. Shang, Y. Zhu, Y.C. Liang, R.C. Guan, D. Xu, (2019), "

2849: 2323: 2316: 2268: 2248: 1318: 926: 560: 465: 5265:""Extending and Customizing Content Discovery for the Legal Academic Com" by Sima Mirkin" 3532:. Proceedings of the 16th ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining. 1967: 802:

One example of a mobile recommender system are the approaches taken by companies such as

5540: 4433: 2671: 2393: 2211:

Automated detection and exposure of behavior-based relationships between browsable items

429: 4950:

Breitinger, Corinna; Langer, Stefan; Lommatzsch, Andreas; Gipp, Bela (March 12, 2016).

3361:"SlateQ: A Tractable Decomposition for Reinforcement Learning with Recommendation Sets" 2806:. UMAP '17. Bratislava, Slovakia: Association for Computing Machinery. pp. 32–40. 1430: 1275: 845: 678: 625: 380: 376: 151: 5055: 4951: 4555:"A Troubling Analysis of Reproducibility and Progress in Recommender Systems Research" 4212:. Lecture Notes of Computer Science (LNCS). Vol. 8092. Springer. pp. 390–394 3217:

Kang, Wang-Cheng; McAuley, Julian (2018). "Self-Attentive Sequential Recommendation".

2781:

Discovery of Hidden Similarity on Collaborative Filtering to Overcome Sparsity Problem

2607: 2564: 5631: 5593: 5108: 5073: 5046:

Verma, P.; Sharma, S. (2020). "Artificial Intelligence based Recommendation System".

4756: 4659: 4596: 4505: 4490: 4132: 3831: 3533: 3380:"Reinforcement Learning to Optimize Long-term User Engagement in Recommender Systems" 3378:

Zou, Lixin; Xia, Long; Ding, Zhuoye; Song, Jiaxing; Liu, Weidong; Yin, Dawei (2019).

3148: 2891: 2631: 2473: 2427: 2148: 2034: 1911: 1390: 1143: 982: 970: 882: 826: 820: 662: 514:

Obtaining a list of items that a user has listened to or watched on his/her computer.

326: 294: 290: 263: 177: 5032: 4838: 4725:

Sun, Zhu; Yu, Di; Fang, Hui; Yang, Jie; Qu, Xinghua; Zhang, Jie; Geng, Cong (2020).

3928: 3814: 3590: 3464: 3419: 3324: 3279: 2719: 2383: 2381: 2370: 1949: 1643: 776:. This system combines a content-based technique and a contextual bandit algorithm. 5291:"Mendeley, Elsevier and the importance of content discovery to academic publishers" 4892: 4525:"Sponsored vs. Organic (Research Paper) Recommendations and the Impact of Labeling" 4410: 4336: 4247: 4047: 3504: 3203: 2800:

Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization

2592: 2527: 2335:

Resnick, Paul, Neophytos Iacovou, Mitesh Suchak, Peter Bergström, and John Riedl. "

2197: 1790: 1380: 1306: 596: 580: 484:

Presenting two items to a user and asking him/her to choose the better one of them.

392: 5197:"Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications" 4983: 4933: 4464: 4447:

Möller, Judith; Trilling, Damian; Helberger, Natali; van Es, Bram (July 3, 2018).

4350:

Joeran Beel; Stefan Langer; Andreas Nürnberger; Marcel Genzmehr (September 2013).

4201:

Joeran Beel; Stefan Langer; Marcel Genzmehr; Andreas Nürnberger (September 2013).

3170:. CIKM '18. Torino, Italy: Association for Computing Machinery. pp. 843–852. 2829: 2140: 1843: 852:. 4-Tell, Inc. created a Netflix project–derived solution for ecommerce websites. 4808: 4726: 4177: 3952: 3379: 3244:

Proceedings of the 2017 ACM on Conference on Information and Knowledge Management

3003:, Semantic Web – Interoperability, Usability, Applicability 1 (2010) 1, IOS Press 2940: 2923: 2050: 2016: 1979: 1732: 836:

Predictive accuracy is substantially improved when blending multiple predictors.

5548:

Jannach, Dietmar; Markus Zanker; Alexander Felfernig; Gerhard Friedrich (2010).

4233:"Is seeing believing?: how recommender system interfaces affect users' opinions" 3856: 2558:"Research paper recommender system evaluation: A quantitative literature survey" 1711: 1475: 1435: 1226:: what specify time and date or a season that a user interacts with the platform 1079: 1007: 890: 646: 388: 368: 5223: 5196: 5143: 5126: 5100: 4124: 3985: 2261: 1548: 1531: 811:

points along a route, with the goal of optimizing occupancy times and profits.

612: 517:

Analyzing the user's social network and discovering similar likes and dislikes.

5443:"YouTube Adding Experimental Community Notes Feature to Battle Misinformation" 5181: 4967: 4401: 3164:"Recurrent Neural Networks with Top-k Gains for Session-based Recommendations" 2623: 2362: 2081:

Conference on Research and Development in Information Retrieval (SIGIR 2002).

2065: 1895: 1626: 1609: 886: 791: 632:

in order to estimate the probability that the user is going to like the item.

553: 4975: 4482: 3582: 2922:

Wang, Donghui; Liang, Yanchun; Xu, Dong; Feng, Xiaoyue; Guan, Renchu (2018).

2556:

Beel, J.; Langer, S.; Genzmehr, M.; Gipp, B.; Breitinger, C. (October 2013).

1903: 1635: 5622:

Proceedings of the Eighteenth National Conference on Artificial Intelligence

5006: 4915: 4874: 4820: 4738: 4700: 4633: 4075: 4029: 3806: 3776: 3401: 3306: 3261: 3185: 3129: 2811: 2735:"Application of Dimensionality Reduction in Recommender System A Case Study" 2734: 2574: 2509: 2035:"A survey of active learning in collaborative filtering recommender systems" 1686: 1584: 1338: 1286:

Google Scholar provides an 'Updates' tool that suggests articles by using a

1211: 481:

Asking a user to rank a collection of items from favorite to least favorite.

172: 64: 54: 5587:

Robert M. Bell; Jim Bennett; Yehuda Koren & Chris Volinsky (May 2009).

1782: 17: 5598: 5318:"Social media algorithms can be redesigned to bridge divides — here's how" 4240:

Proceedings of the SIGCHI conference on Human factors in computing systems

3910: 3609:"A $ 1 Million Research Bargain for Netflix, and Maybe a Model for Others" 3294: 3239: 3163: 2794: 2465: 5473:

Belfer Center for Science and International Affairs at Harvard University

4641: 4588: 4554: 4079: 3360: 2795:"A Multi-Armed Bandit Model Selection for Cold-Start User Recommendation" 2419: 885:

of recommender systems, and compare different approaches, three types of

868:, led to the cancellation of a second Netflix Prize competition in 2010. 825:

One of the events that energized research in recommender systems was the

704:: Choosing among recommendation components and applying the selected one. 698:: Combining the score of different recommendation components numerically. 641: 302:

with relevant academic content and serendipitously discover new content.

282: 274: 258: 5512:

Kim Falk (d 2019), Practical Recommender Systems, Manning Publications,

5014: 4318: 3456: 2711: 1869:

Content-based book recommendation using learning for text categorization

1710:

Felfernig, Alexander; Isak, Klaus; Szabo, Kalman; Zachar, Peter (2007).

1670:

Chen, Hung-Hsuan; Gou, Liang; Zhang, Xiaolong; Giles, Clyde Lee (2011).

5120: 5118: 4611: 4105: 3293:

Liu, Qiao; Zeng, Yifu; Mokhosi, Refuoe; Zhang, Haibin (July 19, 2018).

2672:

Empirical Analysis of Predictive Algorithms for Collaborative Filtering

2647:

Empirical analysis of predictive algorithms for collaborative filtering

2304:

Social information filtering: algorithms for automating "word of mouth"

1322: 1314: 1104: in this section. Unsourced material may be challenged and removed. 684: 449:

similarity or item similarity in recommender systems. For example, the

437:

One approach to the design of recommender systems that has wide use is

322: 286: 4610:

Ferrari Dacrema, Maurizio; Cremonesi, Paolo; Jannach, Dietmar (2019).

2981: 2924:"A content-based recommender system for computer science publications" 2768:. AAAI Workshop in Semantic Web Personalization, San Jose, California. 2337:

GroupLens: an open architecture for collaborative filtering of netnews

2185:

System and method for providing access to data using customer profiles

1757:"How to tame the flood of literature : Nature News & Comment" 1658:

ExpertSeer: a Keyphrase Based Expert Recommender for Digital Libraries

5619:

Content-Boosted Collaborative Filtering for Improved Recommendations.

1532:"A systematic review and research perspective on recommender systems" 1310: 1279: 521:

Collaborative filtering approaches often suffer from three problems:

4580: 3978:

Proceedings of the 2017 SIAM International Conference on Data Mining

3031: 3014: 2843:

Collaborative Recommendations Using Item-to-Item Similarity Mappings

2262:

Newsgroup Clustering Based On User Behavior-A Recommendation Algebra

1966:

Rubens, Neil; Elahi, Mehdi; Sugiyama, Masashi; Kaplan, Dain (2016).

1941: 1773: 1756: 360:

Recommender systems have been the focus of several granted patents.

5417:"YouTube's community notes feature rips a page out of X's playbook" 5213: 5172: 4690: 4624: 4571: 4231:

Cosley, D.; Lam, S.K.; Albert, I.; Konstan, J.A.; Riedl, J (2003).

3573: 3392: 3344: 3252: 3223: 3176: 3101: 3075: 2957:"Online Recommender Systems – How Does a Website Know What I Want?" 2198:

Playlist-based detection of similar digital works and work creators

1882:

Haupt, Jon (June 1, 2009). "Last.fm: People-Powered Online Radio".

1657: 5574:

Computing Taste: Algorithms and the Makers of Music Recommendation

4504:

Montaner, Miquel; López, Beatriz; de la Rosa, Josep Lluís (2002).

4148:

Proceedings of the 14th international conference on World Wide Web

4104:

Cañamares, Rocío; Castells, Pablo; Moffat, Alistair (March 2020).

2766:

Using viewing time to infer user preference in recommender systems

2655: 1577:

Proceedings of the 22nd International Conference on World Wide Web

428: 5271:. Digital Commons @ American University Washington College of Law 4523:

Beel, Joeran, Langer, Stefan, Genzmehr, Marcel (September 2013).

2317:

Recommending and evaluating choices in a virtual community of use

1608:

Baran, Remigiusz; Dziech, Andrzej; Zeja, Andrzej (June 1, 2018).

1181:: 'Distance' measures how far apart users are in this space. See 3697:"Netflix Spilled Your Brokeback Mountain Secret, Lawsuit Claims" 2606:

Beel, J.; Gipp, B.; Langer, S.; Breitinger, C. (July 26, 2015).

857: 807: 803: 606:

A history of the user's interaction with the recommender system.

384: 1464:"Recommender Systems: Techniques, Applications, and Challenges" 579:

In this system, keywords are used to describe the items, and a

487:

Asking a user to create a list of items that he/she likes (see

4867:

Proceedings of the fifth ACM conference on Recommender systems

4809:"Two-stage Model for Automatic Playlist Continuation at Scale" 2315:

Hill, Will, Larry Stead, Mark Rosenstein, and George Furnas. "

1073: 864:

by releasing the datasets. This, as well as concerns from the

772:, a system which models the context-aware recommendation as a 673:

Most recommender systems now use a hybrid approach, combining

572:

Another common approach when designing recommender systems is

433:

An example of collaborative filtering based on a rating system

395:, also at MIT, whose work with GroupLens was awarded the 2010 4616:

Proceedings of the 13th ACM Conference on Recommender Systems

4168:. In Ricci, Francesco; Rokach, Lior; Shapira, Bracha (eds.). 3885:"Why batch and user evaluations do not give the same results" 2196:

Harbick, Andrew V., Ryan J. Snodgrass, and Joel R. Spiegel. "

1970:. In Ricci, Francesco; Rokach, Lior; Shapira, Bracha (eds.). 1466:. In Ricci, Francesco; Rokach, Lior; Shapira, Bracha (eds.). 4999:

Proceedings of the 8th ACM Conference on Recommender systems

3162:

Hidasi, Balázs; Karatzoglou, Alexandros (October 17, 2018).

2209:

Linden, Gregory D., Brent Russell Smith, and Nida K. Zada. "

2183:

Herz, Frederick, Lyle Ungar, Jian Zhang, and David Wachob. "

471:

Examples of explicit data collection include the following:

5624:(AAAI-2002), pp. 187–192, Edmonton, Canada, July 2002. 3123:. Association for Computing Machinery. pp. 2291–2299. 2283:"A digital bookshelf: original work on recommender systems" 511:

Keeping a record of the items that a user purchases online.

2123:

Bi, Xuan; Qu, Annie; Wang, Junhui; Shen, Xiaotong (2017).

1138:

Recommendation systems widely adopt AI techniques such as

840:

Consequently, our solution is an ensemble of many methods.

790:

Mobile recommender systems make use of internet-accessing

741:, Transformers, and other deep-learning-based approaches. 27:

Information filtering system to predict users' preferences

4952:"Towards reproducibility in recommender-systems research" 4813:

Proceedings of the ACM Recommender Systems Challenge 2018

4382:"Recommender systems: from algorithms to user experience" 2645:

John S. Breese; David Heckerman & Carl Kadie (1998).

2608:"Research Paper Recommender Systems: A Literature Survey" 1681:. Association for Computing Machinery. pp. 231–240. 1672:"CollabSeer: a search engine for collaboration discovery" 1579:. Association for Computing Machinery. pp. 505–514. 599:, the system mostly focuses on two types of information: 505:

Observing the items that a user views in an online store.

5001:. RecSys '14. New York, NY, USA: ACM. pp. 129–136. 4869:. RecSys '11. New York, NY, USA: ACM. pp. 133–140. 3976:

Basaran, Daniel; Ntoutsi, Eirini; Zimek, Arthur (2017).

3013:

Gomez-Uribe, Carlos A.; Hunt, Neil (December 28, 2015).

2733:

Sarwar, B.; Karypis, G.; Konstan, J.; Riedl, J. (2000).

1462:

Ricci, Francesco; Rokach, Lior; Shapira, Bracha (2022).

4164:

Castells, Pablo; Hurley, Neil J.; Vargas, Saúl (2015).

3478:

Gediminas Adomavicius; Nikos Manouselis; YoungOk Kwon.

2670:

Breese, John S.; Heckerman, David; Kadie, Carl (1998).

1806:"Netflix Revamps iPad App to Improve Content Discovery" 1712:"The VITA Financial Services Sales Support Environment" 310:

Recommender systems usually make use of either or both

4910:. RepSys '13. New York, NY, USA: ACM. pp. 23–28. 4779:

WSDM '21: ACM Conference on Web Search and Data Mining

3945:

Research and Advanced Technology for Digital Libraries

1070:

Artificial intelligence applications in recommendation

281:

which uses recommender system tools. It utilizes user

5269:

Articles in Law Reviews & Other Academic Journals

4014:. RepSys '13. New York, NY, USA: ACM. pp. 7–14. 3883:

Turpin, Andrew H.; Hersh, William (January 1, 2001).

3725:. Netflix Prize Forum. March 12, 2010. Archived from 2033:

Elahi, Mehdi; Ricci, Francesco; Rubens, Neil (2016).

5377:"Elon Musk keeps Birdwatch alive — under a new name" 4106:"Offline Evaluation Options for Recommender Systems" 1219:

boost user experience. Following are some examples:

5160:

IEEE Transactions on Knowledge and Data Engineering

2764:Parsons, J.; Ralph, P.; Gallagher, K. (July 2004). 2398:

IEEE Transactions on Knowledge and Data Engineering

977:, and every attempt to introduce any level of user 5375:Smalley, Alex Mahadevan, Seth (November 8, 2022). 3747:Lathia, N., Hailes, S., Capra, L., Amatriain, X.: 3518: 3516: 3019:ACM Transactions on Management Information Systems 2187:." U.S. Patent 8,056,100, issued November 8, 2011. 2071:Methods and Metrics for Cold-Start Recommendations 5316:Thorburn, Luke; Ovadya, Aviv (October 31, 2023). 3509:(Ph.D.), Institut National des Télécommunications 2779:Sanghack Lee and Jihoon Yang and Sung-Yong Park, 2493:Beel, J.; Genzmehr, M.; Gipp, B. (October 2013). 1930:ACM Transactions on Knowledge Discovery from Data 1470:(3 ed.). New York: Springer. pp. 1–35. 475:Asking a user to rate an item on a sliding scale. 5127:"Artificial intelligence in recommender systems" 4731:Fourteenth ACM Conference on Recommender Systems 4682:Fourteenth ACM Conference on Recommender Systems 4424:Ricci F, Rokach L, Shapira B, Kantor BP (2011). 1871:. In Workshop Recom. Sys.: Algo. and Evaluation. 1501:"How Computers Know What We Want — Before We Do" 353:Recommender systems are a useful alternative to 4159: 4157: 4061:Cañamares, Rocío; Castells, Pablo (July 2018). 3240:"Neural Attentive Session-based Recommendation" 2213:." U.S. Patent 9,070,156, issued June 30, 2015. 2200:." U.S. Patent 8,468,046, issued June 18, 2013. 2129:Journal of the American Statistical Association 2074:. Proceedings of the 25th Annual International 1561: 1559: 848:, a recommendation engine that's active in the 4166:"Novelty and Diversity in Recommender Systems" 3891:. SIGIR '01. New York, NY, USA: ACM. pp. 3833:User Modeling, Adaptation, and Personalization 3602: 3600: 3506:DRARS, A Dynamic Risk-Aware Recommender System 2165:." U.S. Patent 7,222,085, issued May 22, 2007. 745:Reinforcement learning for recommender systems 4172:(2 ed.). Springer US. pp. 881–918. 3526:An Energy-Efficient Mobile Recommender System 1974:(2 ed.). Springer US. pp. 809–846. 1961: 1959: 1834:. In Claude Sammut; Geoffrey I. Webb (eds.). 202: 8: 5400:: CS1 maint: multiple names: authors list ( 4474:11245.1/4242e2e0-3beb-40a0-a6cb-d8947a13efb4 2028: 2026: 1317:has also used this approach for manging its 253:Typically, the suggestions refer to various 5240:Introduction to natural language processing 3628:"The BellKor solution to the Netflix Prize" 4956:User Modeling and User-Adapted Interaction 4389:User Modeling and User-Adapted Interaction 4270:User Modeling and User-Adapted Interaction 2612:International Journal on Digital Libraries 1750: 1748: 1746: 1744: 1742: 1457: 1455: 1453: 1451: 443:matrix factorization (recommender systems) 209: 195: 31: 5222: 5212: 5171: 5142: 4699: 4689: 4623: 4570: 4472: 4400: 4308: 4019: 3900: 3846: 3749:Temporal diversity in recommender systems 3572: 3446: 3391: 3343: 3251: 3222: 3175: 3128: 3100: 3074: 3030: 2939: 2701: 2654: 2455: 2409: 2118: 2116: 1827:Melville, Prem; Sindhwani, Vikas (2010). 1772: 1625: 1547: 1120:Learn how and when to remove this message 4506:"Developing trust in recommender agents" 4453:Information, Communication & Society 3661:"Mátrixfaktorizáció one million dollars" 1968:"Active Learning in Recommender Systems" 4559:ACM Transactions on Information Systems 3626:R. Bell; Y. Koren; C. Volinsky (2007). 3114: 3112: 2753:. International J. Man-Machine Studies. 2302:Shardanand, Upendra, and Pattie Maes. " 2064:Andrew I. Schein; Alexandrin Popescul; 1656:H. Chen, A. G. Ororbia II, C. L. Giles 1447: 691:Some hybridization techniques include: 159: 123: 72: 41: 34: 5589:"The Million Dollar Programming Prize" 5393: 5340: 5338: 4945: 4943: 4855:, Deep Learning Re-Work SF Summit 2018 4297:IEEE Educational Activities Department 4285:"Privacy risks in recommender systems" 3001:The Knowledge Reengineering Bottleneck 2251:. Syslab Working Paper 179 (1990). " 4853:Deep Learning for Recommender Systems 4618:. RecSys '19. ACM. pp. 101–109. 3878: 3876: 3788: 3786: 3090: 3088: 3086: 2751:User Models: Theory, Method, Practice 2488: 2486: 2484: 2125:"A group-specific recommender system" 1923: 1921: 1361:ACM Conference on Recommender Systems 1301:Criticism of Google § Algorithms 1238:: information from outer social media 183:ACM Conference on Recommender Systems 7: 5551:Recommender Systems: An Introduction 5523:Bharat Bhasker; K. Srikumar (2010). 3480:"Multi-Criteria Recommender Systems" 3064: 3062: 2987:Journal of Medical Internet Research 1530:Roy, Deepjyoti; Dutta, Mala (2022). 1102:adding citations to reliable sources 391:at MIT, Will Hill at Bellcore, and 3795:ACM SIGKDD Explorations Newsletter 3607:Lohr, Steve (September 22, 2009). 2955:Blanda, Stephanie (May 25, 2015). 1884:Music Reference Services Quarterly 1867:R. J. Mooney & L. Roy (1999). 1195:Forming Predictive Recommendations 896:The commonly used metrics are the 754:Multi-criteria recommender systems 508:Analyzing item/user viewing times. 25: 5617:, and Ramadass Nagarajan. (2002) 5526:Recommender Systems in E-Commerce 5056:10.1109/ICACCCN51052.2020.9362962 4261:Pu, P.; Chen, L.; Hu, R. (2012). 3659:Bodoky, Thomas (August 6, 2009). 2863:Recommender Systems: The Textbook 1614:Multimedia Tools and Applications 732:Session-based recommender systems 669:Hybrid recommendations approaches 603:A model of the user's preference. 101:Item-item collaborative filtering 5415:Shanklin, Will (June 17, 2024). 3015:"The Netflix Recommender System" 2281:Karlgren, Jussi (October 2017). 1836:Encyclopedia of Machine Learning 1078: 5238:Eisenstein, J. (October 2019). 5131:Complex and Intelligent Systems 3386:. KDD '19. pp. 2810–2818. 2271:." SICS Research Report (1994). 2242:An Algebra for Recommendations. 1154:KNN-based collaborative filters 1089:needs additional citations for 457:as first implemented by Allen. 5576:. University of Chicago Press. 4851:Yves Raimond, Justin Basilico 3561:Journal of Information Science 3047:Hybrid Web Recommender Systems 2351:Artificial Intelligence Review 1838:. Springer. pp. 829–838. 1804:Analysis (December 14, 2011). 891:online evaluations (A/B tests) 858:Internet Movie Database (IMDb) 763:Risk-aware recommender systems 1: 5653:Social information processing 5467:Ovadya, Aviv (May 17, 2022). 5441:Novak, Matt (June 17, 2024). 5345:Ovadya, Aviv (May 17, 2022). 5263:Mirkin, Sima (June 4, 2014). 4465:10.1080/1369118X.2018.1444076 2961:American Mathematical Society 2677:(Report). Microsoft Research. 2141:10.1080/01621459.2016.1219261 1844:10.1007/978-0-387-30164-8_705 1767:(7516). Nature.com: 129–130. 1717:. In William Cheetham (ed.). 1499:Lev Grossman (May 27, 2010). 889:are available: user studies, 786:Location based recommendation 659:Multimodal sentiment analysis 645:various techniques including 525:, scalability, and sparsity. 493:or other similar techniques). 387:, and research groups led by 4426:Recommender systems handbook 4380:Konstan JA, Riedl J (2012). 4361:. Springer. pp. 400–404 4178:10.1007/978-1-4899-7637-6_26 4170:Recommender Systems Handbook 3953:10.1007/978-3-319-24592-8_12 3503:Bouneffouf, Djallel (2013), 2941:10.1016/j.knosys.2018.05.001 2051:10.1016/j.cosrev.2016.05.002 2017:10.1016/j.knosys.2011.06.005 1980:10.1007/978-1-4899-7637-6_24 1972:Recommender Systems Handbook 1468:Recommender Systems Handbook 1396:Information filtering system 1254:singular value decomposition 1168:. The ideas are as follows: 862:Video Privacy Protection Act 534:multi-armed bandit algorithm 247:information filtering system 5125:Zhang, Q. (February 2021). 3857:10.1007/978-3-642-38844-6_3 3678:Rise of the Netflix Hackers 2861:Aggarwal, Charu C. (2016). 2068:; David M. Pennock (2002). 1476:10.1007/978-1-0716-2197-4_1 1258:latent Dirichlet allocation 1243:Natural language processing 1148:natural language processing 893:, and offline evaluations. 132:Collaborative search engine 5669: 5224:10.1109/JPROC.2021.3060483 5144:10.1007/s40747-020-00212-w 5101:10.1007/s10639-019-10063-9 4125:10.1007/s10791-020-09371-3 3986:10.1137/1.9781611974973.44 2783:, Discovery Science, 2007. 2267:February 27, 2021, at the 1755:jobs (September 3, 2014). 1549:10.1186/s40537-022-00592-5 1356:Algorithmic radicalization 1298: 1269:Academic content discovery 818: 783: 780:Mobile recommender systems 630:artificial neural networks 422: 397:ACM Software Systems Award 271:content discovery platform 137:Content discovery platform 5182:10.1109/TKDE.2022.3145690 4968:10.1007/s11257-016-9174-x 4402:10.1007/s11257-011-9112-x 3683:January 24, 2012, at the 2624:10.1007/s00799-015-0156-0 1896:10.1080/10588160902816702 1627:10.1007/s11042-017-5014-1 1208:artificial neural network 1185:for computational details 856:with film ratings on the 739:recurrent neural networks 255:decision-making processes 5469:"Bridging-Based Ranking" 5347:"Bridging-Based Ranking" 5195:Samek, W. (March 2021). 4119:(4). Springer: 387–410. 3583:10.1177/0165551518792213 3435:IEEE Intelligent Systems 1660:, in arXiv preprint 2015 1406:Media monitoring service 1250:latent semantic analysis 1230:User Navigation Patterns 866:Federal Trade Commission 499:implicit data collection 478:Asking a user to search. 453:(k-NN) approach and the 96:Implicit data collection 91:Dimensionality reduction 5201:Proceedings of the IEEE 5007:10.1145/2645710.2645746 4916:10.1145/2532508.2532513 4875:10.1145/2043932.2043958 4821:10.1145/3267471.3267480 4739:10.1145/3383313.3412489 4733:. ACM. pp. 23–32. 4701:10.1145/3383313.3412488 4634:10.1145/3298689.3347058 4289:IEEE Internet Computing 4076:10.1145/3209978.3210014 4030:10.1145/2532508.2532511 3807:10.1145/3137597.3137601 3534:New York City, New York 3402:10.1145/3292500.3330668 3307:10.1145/3219819.3219950 3262:10.1145/3132847.3132926 3186:10.1145/3269206.3271761 3130:10.1145/3394486.3403278 2928:Knowledge-Based Systems 2812:10.1145/3079628.3079681 2575:10.1145/2532508.2532512 2510:10.1145/2532508.2532511 2363:10.1023/A:1022850703159 2039:Computer Science Review 2005:Knowledge-Based Systems 1687:10.1145/1998076.1998121 1585:10.1145/2488388.2488433 1376:Collective intelligence 1371:Collaborative filtering 1159:Collaborative filtering 1133:Artificial intelligence 961:Recommender persistence 902:root mean squared error 675:collaborative filtering 574:content-based filtering 568:Content-based filtering 556:'s recommender system. 501:include the following: 439:collaborative filtering 425:Collaborative filtering 419:Collaborative filtering 316:knowledge-based systems 312:collaborative filtering 142:Decision support system 86:Collaborative filtering 50:Collective intelligence 3723:"Netflix Prize Update" 1721:. pp. 1692–1699. 1426:Preference elicitation 1416:Personalized marketing 1386:Enterprise bookmarking 1236:External Social Trends 1038:reproducibility crisis 842: 490:Rocchio classification 434: 111:Preference elicitation 73:Methods and challenges 5643:Mass media monitoring 5572:Seaver, Nick (2022). 5543:on September 1, 2010. 4815:. ACM. pp. 1–6. 4781:. ACM. Archived from 4295:(6). Piscataway, NJ: 4113:Information Retrieval 3911:10.1145/383952.383992 2466:10.1145/963770.963772 1829:"Recommender Systems" 1401:Information explosion 1274:search tools such as 1264:Specific applications 1189:Identifying Neighbors 834: 795:generality problems. 784:Further information: 651:information retrieval 590:information filtering 586:information retrieval 432: 229:(sometimes replacing 227:recommendation system 5297:on November 17, 2014 5050:. pp. 669–673. 4684:. pp. 240–248. 4242:. pp. 585–592. 3980:. pp. 390–398. 3779:. September 6, 2013. 3729:on November 27, 2011 2897:. Springer. p. 2749:Allen, R.B. (1990). 2444:ACM Trans. Inf. Syst 2420:10.1109/TKDE.2005.99 1224:Time and Seasonality 1183:statistical distance 1179:Statistical Distance 1098:improve this article 906:precision and recall 877:Performance measures 618:Bayesian Classifiers 339:Music Genome Project 245:), is a subclass of 147:Music Genome Project 106:Matrix factorization 5648:Recommender systems 5638:Information systems 5581:Scientific articles 5568:on August 31, 2015. 5356:. pp. 1, 14–28 4512:. pp. 304–305. 4434:2011rsh..book.....R 4319:10.1109/4236.968832 3777:"MovieLens dataset" 3765:. pp. 225–231. 3703:. December 17, 2009 3457:10.1109/mis.2011.33 2712:10.1109/MC.2009.263 1620:(11): 14077–14091. 1536:Journal of Big Data 1421:Personalized search 1411:Pattern recognition 1343:internet television 1327:deliberative groups 1309:. Examples include 1173:Data Representation 1166:K-nearest neighbors 455:Pearson Correlation 233:with terms such as 36:Recommender systems 5494:The New Face of TV 5353:Harvard University 4534:. pp. 395–399 3613:The New York Times 3540:. pp. 899–908 3052:2014-09-12 at the 2848:2015-03-16 at the 2569:. pp. 15–22. 2322:2018-12-21 at the 2260:Karlgren, Jussi. " 2247:2024-05-25 at the 2240:Karlgren, Jussi. " 2135:(519): 1344–1353. 1025:click-through rate 931:click-through rate 898:mean squared error 655:sentiment analysis 451:k-nearest neighbor 435: 273:is an implemented 223:recommender system 168:GroupLens Research 5615:Raymond J. Mooney 5561:978-0-521-49336-9 5536:978-0-07-068067-8 5351:Belfer Center at 5065:978-1-7281-8337-4 5024:978-1-4503-2668-1 4925:978-1-4503-2465-6 4884:978-1-4503-0683-6 4830:978-1-4503-6586-4 4788:on March 25, 2021 4748:978-1-4503-7583-2 4711:978-1-4503-7583-2 4651:978-1-4503-6243-6 4428:. pp. 1–35. 4328:978-1-58113-561-9 4187:978-1-4899-7637-6 4150:. pp. 22–32. 4085:on April 14, 2021 4039:978-1-4503-2465-6 3995:978-1-61197-497-3 3962:978-3-319-24591-1 3920:978-1-58113-331-8 3866:978-3-642-38843-9 3492:on June 30, 2014. 3411:978-1-4503-6201-6 3316:978-1-4503-5552-0 3271:978-1-4503-4918-5 3195:978-1-4503-6014-2 3140:978-1-4503-7998-4 2908:978-3-540-72078-2 2887:Peter Brusilovsky 2872:978-3-319-29657-9 2821:978-1-4503-4635-1 2584:978-1-4503-2465-6 2537:on April 17, 2016 2519:978-1-4503-2465-6 2504:. pp. 7–14. 2388:Adomavicius, G.; 2161:Stack, Charles. " 1989:978-1-4899-7637-6 1853:978-0-387-30164-8 1485:978-1-0716-2196-7 1288:statistical model 1130: 1129: 1122: 992:User demographics 815:The Netflix Prize 355:search algorithms 219: 218: 116:Similarity search 16:(Redirected from 5660: 5610: 5608: 5606: 5597:. Archived from 5569: 5564:. Archived from 5544: 5539:. Archived from 5496: 5491: 5485: 5484: 5482: 5480: 5475:. pp. 21–23 5464: 5458: 5457: 5455: 5453: 5438: 5432: 5431: 5429: 5427: 5412: 5406: 5405: 5399: 5391: 5389: 5387: 5372: 5366: 5365: 5363: 5361: 5342: 5333: 5332: 5330: 5328: 5313: 5307: 5306: 5304: 5302: 5293:. Archived from 5287: 5281: 5280: 5278: 5276: 5260: 5254: 5253: 5235: 5229: 5228: 5226: 5216: 5192: 5186: 5185: 5175: 5166:(5): 4425–4445. 5155: 5149: 5148: 5146: 5122: 5113: 5112: 5095:(4): 2635–2664. 5089:Educ Inf Technol 5084: 5078: 5077: 5043: 5037: 5036: 4994: 4988: 4987: 4947: 4938: 4937: 4903: 4897: 4896: 4862: 4856: 4849: 4843: 4842: 4804: 4798: 4797: 4795: 4793: 4787: 4776: 4767: 4761: 4760: 4722: 4716: 4715: 4703: 4693: 4677: 4671: 4670: 4668: 4666: 4627: 4607: 4601: 4600: 4574: 4550: 4544: 4543: 4541: 4539: 4529: 4520: 4514: 4513: 4501: 4495: 4494: 4476: 4444: 4438: 4437: 4421: 4415: 4414: 4404: 4386: 4377: 4371: 4370: 4368: 4366: 4356: 4347: 4341: 4340: 4312: 4280: 4274: 4273: 4267: 4258: 4252: 4251: 4237: 4228: 4222: 4221: 4219: 4217: 4207: 4198: 4192: 4191: 4161: 4152: 4151: 4143: 4137: 4136: 4110: 4101: 4095: 4094: 4092: 4090: 4084: 4078:. Archived from 4069: 4058: 4052: 4051: 4023: 4006: 4000: 3999: 3973: 3967: 3966: 3939: 3933: 3932: 3904: 3880: 3871: 3870: 3850: 3836: 3825: 3819: 3818: 3790: 3781: 3780: 3773: 3767: 3766: 3758: 3752: 3745: 3739: 3738: 3736: 3734: 3719: 3713: 3712: 3710: 3708: 3693: 3687: 3675: 3669: 3668: 3656: 3650: 3649: 3647: 3645: 3640:on March 4, 2012 3639: 3633:. Archived from 3632: 3623: 3617: 3616: 3604: 3595: 3594: 3576: 3556: 3550: 3549: 3547: 3545: 3531: 3520: 3511: 3510: 3500: 3494: 3493: 3491: 3485:. Archived from 3484: 3475: 3469: 3468: 3450: 3430: 3424: 3423: 3395: 3375: 3369: 3368: 3356: 3350: 3349: 3347: 3335: 3329: 3328: 3290: 3284: 3283: 3255: 3235: 3229: 3228: 3226: 3214: 3208: 3207: 3179: 3159: 3153: 3152: 3132: 3116: 3107: 3106: 3104: 3092: 3081: 3080: 3078: 3066: 3057: 3043: 3037: 3036: 3034: 3010: 3004: 2999:Rinke Hoekstra, 2997: 2991: 2990:, 21 (5): e12957 2978: 2972: 2971: 2969: 2967: 2952: 2946: 2945: 2943: 2919: 2913: 2912: 2896: 2893:The Adaptive Web 2883: 2877: 2876: 2858: 2852: 2840: 2834: 2833: 2805: 2790: 2784: 2777: 2771: 2769: 2761: 2755: 2754: 2746: 2740: 2738: 2730: 2724: 2723: 2705: 2685: 2679: 2678: 2676: 2667: 2661: 2660: 2658: 2642: 2636: 2635: 2603: 2597: 2596: 2562: 2553: 2547: 2546: 2544: 2542: 2536: 2530:. Archived from 2499: 2490: 2479: 2477: 2459: 2439: 2433: 2431: 2413: 2385: 2376: 2374: 2346: 2340: 2333: 2327: 2313: 2307: 2300: 2294: 2293: 2291: 2289: 2278: 2272: 2258: 2252: 2238: 2232: 2229: 2223: 2220: 2214: 2207: 2201: 2194: 2188: 2181: 2175: 2172: 2166: 2159: 2153: 2152: 2120: 2111: 2110: 2108: 2106: 2061: 2055: 2054: 2030: 2021: 2020: 2011:(8): 1310–1316. 2000: 1994: 1993: 1963: 1954: 1953: 1925: 1916: 1915: 1879: 1873: 1872: 1864: 1858: 1857: 1833: 1824: 1818: 1817: 1815: 1813: 1801: 1795: 1794: 1776: 1752: 1737: 1736: 1716: 1707: 1701: 1700: 1676: 1667: 1661: 1654: 1648: 1647: 1629: 1605: 1599: 1598: 1572: 1566: 1563: 1554: 1553: 1551: 1527: 1521: 1520: 1518: 1516: 1507:. Archived from 1496: 1490: 1489: 1459: 1140:machine learning 1125: 1118: 1114: 1111: 1105: 1082: 1074: 1047:RecSys Challenge 850:RecSys community 622:cluster analysis 299:academic journal 211: 204: 197: 32: 21: 5668: 5667: 5663: 5662: 5661: 5659: 5658: 5657: 5628: 5627: 5613:Prem Melville, 5604: 5602: 5601:on May 11, 2009 5586: 5562: 5547: 5537: 5522: 5504: 5502:Further reading 5499: 5492: 5488: 5478: 5476: 5466: 5465: 5461: 5451: 5449: 5440: 5439: 5435: 5425: 5423: 5414: 5413: 5409: 5392: 5385: 5383: 5374: 5373: 5369: 5359: 5357: 5344: 5343: 5336: 5326: 5324: 5315: 5314: 5310: 5300: 5298: 5289: 5288: 5284: 5274: 5272: 5262: 5261: 5257: 5250: 5237: 5236: 5232: 5194: 5193: 5189: 5157: 5156: 5152: 5124: 5123: 5116: 5086: 5085: 5081: 5066: 5045: 5044: 5040: 5025: 4996: 4995: 4991: 4949: 4948: 4941: 4926: 4905: 4904: 4900: 4885: 4864: 4863: 4859: 4850: 4846: 4831: 4806: 4805: 4801: 4791: 4789: 4785: 4774: 4769: 4768: 4764: 4749: 4724: 4723: 4719: 4712: 4679: 4678: 4674: 4664: 4662: 4652: 4609: 4608: 4604: 4581:10.1145/3434185 4552: 4551: 4547: 4537: 4535: 4527: 4522: 4521: 4517: 4503: 4502: 4498: 4446: 4445: 4441: 4423: 4422: 4418: 4384: 4379: 4378: 4374: 4364: 4362: 4354: 4349: 4348: 4344: 4329: 4282: 4281: 4277: 4265: 4260: 4259: 4255: 4235: 4230: 4229: 4225: 4215: 4213: 4205: 4200: 4199: 4195: 4188: 4163: 4162: 4155: 4145: 4144: 4140: 4108: 4103: 4102: 4098: 4088: 4086: 4082: 4067: 4060: 4059: 4055: 4040: 4021:10.1.1.1031.973 4008: 4007: 4003: 3996: 3975: 3974: 3970: 3963: 3941: 3940: 3936: 3921: 3902:10.1.1.165.5800 3882: 3881: 3874: 3867: 3827: 3826: 3822: 3792: 3791: 3784: 3775: 3774: 3770: 3760: 3759: 3755: 3746: 3742: 3732: 3730: 3721: 3720: 3716: 3706: 3704: 3695: 3694: 3690: 3685:Wayback Machine 3676: 3672: 3658: 3657: 3653: 3643: 3641: 3637: 3630: 3625: 3624: 3620: 3606: 3605: 3598: 3558: 3557: 3553: 3543: 3541: 3529: 3522: 3521: 3514: 3502: 3501: 3497: 3489: 3482: 3477: 3476: 3472: 3448:10.1.1.476.6726 3432: 3431: 3427: 3412: 3377: 3376: 3372: 3358: 3357: 3353: 3337: 3336: 3332: 3317: 3292: 3291: 3287: 3272: 3237: 3236: 3232: 3216: 3215: 3211: 3196: 3161: 3160: 3156: 3141: 3118: 3117: 3110: 3094: 3093: 3084: 3068: 3067: 3060: 3054:Wayback Machine 3044: 3040: 3032:10.1145/2843948 3012: 3011: 3007: 2998: 2994: 2979: 2975: 2965: 2963: 2954: 2953: 2949: 2921: 2920: 2916: 2909: 2885: 2884: 2880: 2873: 2860: 2859: 2855: 2850:Wayback Machine 2841: 2837: 2822: 2803: 2792: 2791: 2787: 2778: 2774: 2763: 2762: 2758: 2748: 2747: 2743: 2732: 2731: 2727: 2703:10.1.1.147.8295 2687: 2686: 2682: 2674: 2669: 2668: 2664: 2644: 2643: 2639: 2605: 2604: 2600: 2585: 2560: 2555: 2554: 2550: 2540: 2538: 2534: 2520: 2497: 2492: 2491: 2482: 2441: 2440: 2436: 2411:10.1.1.107.2790 2387: 2386: 2379: 2348: 2347: 2343: 2334: 2330: 2324:Wayback Machine 2314: 2310: 2301: 2297: 2287: 2285: 2280: 2279: 2275: 2269:Wayback Machine 2259: 2255: 2249:Wayback Machine 2239: 2235: 2230: 2226: 2221: 2217: 2208: 2204: 2195: 2191: 2182: 2178: 2173: 2169: 2160: 2156: 2122: 2121: 2114: 2104: 2102: 2100: 2063: 2062: 2058: 2032: 2031: 2024: 2002: 2001: 1997: 1990: 1965: 1964: 1957: 1942:10.1145/3285954 1927: 1926: 1919: 1881: 1880: 1876: 1866: 1865: 1861: 1854: 1831: 1826: 1825: 1821: 1811: 1809: 1803: 1802: 1798: 1774:10.1038/513129a 1754: 1753: 1740: 1729: 1714: 1709: 1708: 1704: 1697: 1674: 1669: 1668: 1664: 1655: 1651: 1607: 1606: 1602: 1595: 1574: 1573: 1569: 1564: 1557: 1529: 1528: 1524: 1514: 1512: 1511:on May 30, 2010 1498: 1497: 1493: 1486: 1461: 1460: 1449: 1445: 1440: 1351: 1335: 1319:community notes 1303: 1297: 1295:Decision-making 1271: 1266: 1245: 1204: 1202:Neural networks 1156: 1126: 1115: 1109: 1106: 1095: 1083: 1072: 1034: 1032:Reproducibility 946: 944:Beyond accuracy 927:conversion rate 879: 874: 846:Gravity R&D 823: 817: 788: 782: 765: 756: 747: 734: 729: 679:knowledge-based 671: 570: 561:social networks 466:data collection 427: 421: 416: 366: 308: 277:recommendation 215: 124:Implementations 28: 23: 22: 15: 12: 11: 5: 5666: 5664: 5656: 5655: 5650: 5645: 5640: 5630: 5629: 5626: 5625: 5611: 5583: 5582: 5578: 5577: 5570: 5560: 5545: 5535: 5520: 5509: 5508: 5503: 5500: 5498: 5497: 5486: 5459: 5433: 5407: 5367: 5334: 5308: 5282: 5255: 5248: 5230: 5207:(3): 247–278. 5187: 5150: 5114: 5079: 5064: 5038: 5023: 4989: 4939: 4924: 4898: 4883: 4857: 4844: 4829: 4799: 4762: 4747: 4717: 4710: 4672: 4650: 4602: 4545: 4515: 4496: 4459:(7): 959–977. 4439: 4416: 4372: 4342: 4327: 4275: 4253: 4223: 4193: 4186: 4153: 4138: 4096: 4053: 4038: 4001: 3994: 3968: 3961: 3934: 3919: 3872: 3865: 3820: 3782: 3768: 3753: 3740: 3714: 3688: 3670: 3651: 3618: 3596: 3567:(3): 387–397. 3551: 3512: 3495: 3470: 3425: 3410: 3370: 3351: 3330: 3315: 3285: 3270: 3230: 3209: 3194: 3154: 3139: 3108: 3082: 3058: 3038: 3005: 2992: 2973: 2947: 2914: 2907: 2878: 2871: 2853: 2835: 2820: 2785: 2772: 2756: 2741: 2725: 2680: 2662: 2637: 2618:(4): 305–338. 2598: 2583: 2548: 2518: 2480: 2457:10.1.1.78.8384 2434: 2404:(6): 734–749. 2377: 2357:(4): 285–330. 2341: 2328: 2308: 2295: 2273: 2253: 2233: 2224: 2215: 2202: 2189: 2176: 2167: 2154: 2112: 2098: 2056: 2022: 1995: 1988: 1955: 1917: 1890:(1–2): 23–24. 1874: 1859: 1852: 1819: 1796: 1738: 1727: 1702: 1695: 1662: 1649: 1600: 1593: 1567: 1555: 1522: 1491: 1484: 1446: 1444: 1441: 1439: 1438: 1433: 1431:Product finder 1428: 1423: 1418: 1413: 1408: 1403: 1398: 1393: 1388: 1383: 1378: 1373: 1368: 1363: 1358: 1352: 1350: 1347: 1334: 1331: 1296: 1293: 1276:Google Scholar 1270: 1267: 1265: 1262: 1244: 1241: 1240: 1239: 1233: 1227: 1203: 1200: 1199: 1198: 1192: 1186: 1176: 1155: 1152: 1128: 1127: 1086: 1084: 1077: 1071: 1068: 1033: 1030: 1029: 1028: 1018: 1012: 1001: 995: 988: 987: 964: 957: 956: 945: 942: 878: 875: 873: 870: 819:Main article: 816: 813: 781: 778: 774:bandit problem 764: 761: 755: 752: 746: 743: 733: 730: 728: 725: 724: 723: 717: 711: 705: 699: 670: 667: 626:decision trees 608: 607: 604: 569: 566: 550: 549: 543: 537: 519: 518: 515: 512: 509: 506: 495: 494: 485: 482: 479: 476: 423:Main article: 420: 417: 415: 412: 381:Jussi Karlgren 377:Jussi Karlgren 365: 362: 343: 342: 335: 307: 304: 291:mobile devices 217: 216: 214: 213: 206: 199: 191: 188: 187: 186: 185: 180: 175: 170: 162: 161: 157: 156: 155: 154: 152:Product finder 149: 144: 139: 134: 126: 125: 121: 120: 119: 118: 113: 108: 103: 98: 93: 88: 83: 75: 74: 70: 69: 68: 67: 62: 57: 52: 44: 43: 39: 38: 26: 24: 14: 13: 10: 9: 6: 4: 3: 2: 5665: 5654: 5651: 5649: 5646: 5644: 5641: 5639: 5636: 5635: 5633: 5623: 5620: 5616: 5612: 5600: 5596: 5595: 5594:IEEE Spectrum 5590: 5585: 5584: 5580: 5579: 5575: 5571: 5567: 5563: 5557: 5553: 5552: 5546: 5542: 5538: 5532: 5528: 5527: 5521: 5519: 5518:9781617292705 5515: 5511: 5510: 5506: 5505: 5501: 5495: 5490: 5487: 5474: 5470: 5463: 5460: 5448: 5444: 5437: 5434: 5422: 5418: 5411: 5408: 5403: 5397: 5382: 5378: 5371: 5368: 5355: 5354: 5348: 5341: 5339: 5335: 5323: 5319: 5312: 5309: 5296: 5292: 5286: 5283: 5270: 5266: 5259: 5256: 5251: 5249:9780262042840 5245: 5242:. MIT press. 5241: 5234: 5231: 5225: 5220: 5215: 5210: 5206: 5202: 5198: 5191: 5188: 5183: 5179: 5174: 5169: 5165: 5161: 5154: 5151: 5145: 5140: 5136: 5132: 5128: 5121: 5119: 5115: 5110: 5106: 5102: 5098: 5094: 5090: 5083: 5080: 5075: 5071: 5067: 5061: 5057: 5053: 5049: 5042: 5039: 5034: 5030: 5026: 5020: 5016: 5012: 5008: 5004: 5000: 4993: 4990: 4985: 4981: 4977: 4973: 4969: 4965: 4962:(1): 69–101. 4961: 4957: 4953: 4946: 4944: 4940: 4935: 4931: 4927: 4921: 4917: 4913: 4909: 4902: 4899: 4894: 4890: 4886: 4880: 4876: 4872: 4868: 4861: 4858: 4854: 4848: 4845: 4840: 4836: 4832: 4826: 4822: 4818: 4814: 4810: 4803: 4800: 4784: 4780: 4773: 4766: 4763: 4758: 4754: 4750: 4744: 4740: 4736: 4732: 4728: 4721: 4718: 4713: 4707: 4702: 4697: 4692: 4687: 4683: 4676: 4673: 4661: 4657: 4653: 4647: 4643: 4642:11311/1108996 4639: 4635: 4631: 4626: 4621: 4617: 4613: 4606: 4603: 4598: 4594: 4590: 4589:11311/1164333 4586: 4582: 4578: 4573: 4568: 4564: 4560: 4556: 4549: 4546: 4533: 4526: 4519: 4516: 4511: 4507: 4500: 4497: 4492: 4488: 4484: 4480: 4475: 4470: 4466: 4462: 4458: 4454: 4450: 4443: 4440: 4435: 4431: 4427: 4420: 4417: 4412: 4408: 4403: 4398: 4395:(1–2): 1–23. 4394: 4390: 4383: 4376: 4373: 4360: 4353: 4346: 4343: 4338: 4334: 4330: 4324: 4320: 4316: 4311: 4310:10.1.1.2.2932 4306: 4302: 4298: 4294: 4290: 4286: 4279: 4276: 4271: 4264: 4257: 4254: 4249: 4245: 4241: 4234: 4227: 4224: 4211: 4204: 4197: 4194: 4189: 4183: 4179: 4175: 4171: 4167: 4160: 4158: 4154: 4149: 4142: 4139: 4134: 4130: 4126: 4122: 4118: 4114: 4107: 4100: 4097: 4081: 4077: 4073: 4066: 4065: 4057: 4054: 4049: 4045: 4041: 4035: 4031: 4027: 4022: 4017: 4013: 4005: 4002: 3997: 3991: 3987: 3983: 3979: 3972: 3969: 3964: 3958: 3954: 3950: 3946: 3938: 3935: 3930: 3926: 3922: 3916: 3912: 3908: 3903: 3898: 3894: 3890: 3886: 3879: 3877: 3873: 3868: 3862: 3858: 3854: 3849: 3848:10.1.1.465.96 3844: 3840: 3835: 3834: 3824: 3821: 3816: 3812: 3808: 3804: 3800: 3796: 3789: 3787: 3783: 3778: 3772: 3769: 3764: 3757: 3754: 3750: 3744: 3741: 3728: 3724: 3718: 3715: 3702: 3698: 3692: 3689: 3686: 3682: 3679: 3674: 3671: 3666: 3662: 3655: 3652: 3636: 3629: 3622: 3619: 3614: 3610: 3603: 3601: 3597: 3592: 3588: 3584: 3580: 3575: 3570: 3566: 3562: 3555: 3552: 3539: 3535: 3528: 3527: 3519: 3517: 3513: 3508: 3507: 3499: 3496: 3488: 3481: 3474: 3471: 3466: 3462: 3458: 3454: 3449: 3444: 3440: 3436: 3429: 3426: 3421: 3417: 3413: 3407: 3403: 3399: 3394: 3389: 3385: 3381: 3374: 3371: 3366: 3362: 3355: 3352: 3346: 3341: 3334: 3331: 3326: 3322: 3318: 3312: 3308: 3304: 3300: 3296: 3289: 3286: 3281: 3277: 3273: 3267: 3263: 3259: 3254: 3249: 3245: 3241: 3234: 3231: 3225: 3220: 3213: 3210: 3205: 3201: 3197: 3191: 3187: 3183: 3178: 3173: 3169: 3165: 3158: 3155: 3150: 3146: 3142: 3136: 3131: 3126: 3122: 3115: 3113: 3109: 3103: 3098: 3091: 3089: 3087: 3083: 3077: 3072: 3065: 3063: 3059: 3055: 3051: 3048: 3045:Robin Burke, 3042: 3039: 3033: 3028: 3024: 3020: 3016: 3009: 3006: 3002: 2996: 2993: 2989: 2988: 2983: 2977: 2974: 2962: 2958: 2951: 2948: 2942: 2937: 2933: 2929: 2925: 2918: 2915: 2910: 2904: 2900: 2895: 2894: 2888: 2882: 2879: 2874: 2868: 2864: 2857: 2854: 2851: 2847: 2844: 2839: 2836: 2831: 2827: 2823: 2817: 2813: 2809: 2802: 2801: 2796: 2789: 2786: 2782: 2776: 2773: 2767: 2760: 2757: 2752: 2745: 2742: 2736: 2729: 2726: 2721: 2717: 2713: 2709: 2704: 2699: 2695: 2691: 2684: 2681: 2673: 2666: 2663: 2657: 2652: 2648: 2641: 2638: 2633: 2629: 2625: 2621: 2617: 2613: 2609: 2602: 2599: 2594: 2590: 2586: 2580: 2576: 2572: 2568: 2567: 2559: 2552: 2549: 2533: 2529: 2525: 2521: 2515: 2511: 2507: 2503: 2496: 2489: 2487: 2485: 2481: 2475: 2471: 2467: 2463: 2458: 2453: 2449: 2445: 2438: 2435: 2429: 2425: 2421: 2417: 2412: 2407: 2403: 2399: 2395: 2392:(June 2005). 2391: 2384: 2382: 2378: 2372: 2368: 2364: 2360: 2356: 2352: 2345: 2342: 2338: 2332: 2329: 2325: 2321: 2318: 2312: 2309: 2305: 2299: 2296: 2284: 2277: 2274: 2270: 2266: 2263: 2257: 2254: 2250: 2246: 2243: 2237: 2234: 2228: 2225: 2219: 2216: 2212: 2206: 2203: 2199: 2193: 2190: 2186: 2180: 2177: 2171: 2168: 2164: 2158: 2155: 2150: 2146: 2142: 2138: 2134: 2130: 2126: 2119: 2117: 2113: 2101: 2099:1-58113-561-0 2095: 2091: 2087: 2083: 2080: 2077: 2073: 2072: 2067: 2066:Lyle H. Ungar 2060: 2057: 2052: 2048: 2044: 2040: 2036: 2029: 2027: 2023: 2018: 2014: 2010: 2006: 1999: 1996: 1991: 1985: 1981: 1977: 1973: 1969: 1962: 1960: 1956: 1951: 1947: 1943: 1939: 1935: 1931: 1924: 1922: 1918: 1913: 1909: 1905: 1901: 1897: 1893: 1889: 1885: 1878: 1875: 1870: 1863: 1860: 1855: 1849: 1845: 1841: 1837: 1830: 1823: 1820: 1807: 1800: 1797: 1792: 1788: 1784: 1780: 1775: 1770: 1766: 1762: 1758: 1751: 1749: 1747: 1745: 1743: 1739: 1734: 1730: 1728:9781577353232 1724: 1720: 1713: 1706: 1703: 1698: 1696:9781450307444 1692: 1688: 1684: 1680: 1673: 1666: 1663: 1659: 1653: 1650: 1645: 1641: 1637: 1633: 1628: 1623: 1619: 1615: 1611: 1604: 1601: 1596: 1594:9781450320351 1590: 1586: 1582: 1578: 1571: 1568: 1562: 1560: 1556: 1550: 1545: 1541: 1537: 1533: 1526: 1523: 1510: 1506: 1502: 1495: 1492: 1487: 1481: 1477: 1473: 1469: 1465: 1458: 1456: 1454: 1452: 1448: 1442: 1437: 1434: 1432: 1429: 1427: 1424: 1422: 1419: 1417: 1414: 1412: 1409: 1407: 1404: 1402: 1399: 1397: 1394: 1392: 1391:Filter bubble 1389: 1387: 1384: 1382: 1379: 1377: 1374: 1372: 1369: 1367: 1364: 1362: 1359: 1357: 1354: 1353: 1348: 1346: 1344: 1340: 1332: 1330: 1328: 1324: 1320: 1316: 1312: 1308: 1302: 1294: 1292: 1289: 1284: 1281: 1277: 1268: 1263: 1261: 1259: 1255: 1251: 1242: 1237: 1234: 1231: 1228: 1225: 1222: 1221: 1220: 1216: 1213: 1209: 1201: 1196: 1193: 1190: 1187: 1184: 1180: 1177: 1174: 1171: 1170: 1169: 1167: 1162: 1160: 1153: 1151: 1149: 1145: 1144:deep learning 1141: 1136: 1134: 1124: 1121: 1113: 1103: 1099: 1093: 1092: 1087:This section 1085: 1081: 1076: 1075: 1069: 1067: 1064: 1060: 1056: 1052: 1048: 1044: 1039: 1031: 1026: 1022: 1019: 1016: 1013: 1009: 1005: 1002: 999: 996: 993: 990: 989: 984: 983:Netflix Prize 980: 976: 972: 971:user profiles 968: 965: 962: 959: 958: 954: 951: 950: 949: 943: 941: 937: 934: 932: 928: 922: 918: 914: 911: 907: 903: 899: 894: 892: 888: 884: 883:effectiveness 876: 871: 869: 867: 863: 859: 853: 851: 847: 841: 839: 833: 830: 828: 827:Netflix Prize 822: 821:Netflix Prize 814: 812: 809: 805: 800: 796: 793: 787: 779: 777: 775: 771: 762: 760: 753: 751: 744: 742: 740: 731: 726: 721: 718: 715: 712: 709: 706: 703: 700: 697: 694: 693: 692: 689: 686: 682: 680: 676: 668: 666: 664: 663:deep learning 660: 656: 652: 648: 643: 637: 633: 631: 627: 623: 619: 614: 605: 602: 601: 600: 598: 593: 591: 587: 582: 577: 575: 567: 565: 562: 557: 555: 547: 544: 541: 538: 535: 531: 528: 527: 526: 524: 516: 513: 510: 507: 504: 503: 502: 500: 492: 491: 486: 483: 480: 477: 474: 473: 472: 469: 467: 463: 458: 456: 452: 446: 444: 440: 431: 426: 418: 413: 411: 409: 405: 400: 398: 394: 390: 386: 382: 378: 373: 370: 363: 361: 358: 356: 351: 348: 340: 336: 332: 331: 330: 328: 327:Pandora Radio 324: 319: 317: 313: 305: 303: 300: 296: 295:set-top boxes 292: 288: 284: 280: 276: 272: 267: 265: 264:online dating 260: 256: 251: 248: 244: 240: 236: 232: 228: 224: 212: 207: 205: 200: 198: 193: 192: 190: 189: 184: 181: 179: 178:Netflix Prize 176: 174: 171: 169: 166: 165: 164: 163: 158: 153: 150: 148: 145: 143: 140: 138: 135: 133: 130: 129: 128: 127: 122: 117: 114: 112: 109: 107: 104: 102: 99: 97: 94: 92: 89: 87: 84: 82: 79: 78: 77: 76: 71: 66: 63: 61: 58: 56: 53: 51: 48: 47: 46: 45: 40: 37: 33: 30: 19: 5621: 5605:December 10, 5603:. Retrieved 5599:the original 5592: 5573: 5566:the original 5550: 5541:the original 5525: 5489: 5477:. Retrieved 5472: 5462: 5450:. Retrieved 5446: 5436: 5424:. Retrieved 5420: 5410: 5384:. Retrieved 5380: 5370: 5358:. Retrieved 5350: 5325:. Retrieved 5321: 5311: 5299:. Retrieved 5295:the original 5285: 5275:December 31, 5273:. Retrieved 5268: 5258: 5239: 5233: 5204: 5200: 5190: 5163: 5159: 5153: 5134: 5130: 5092: 5088: 5082: 5047: 5041: 5015:10486/665450 4998: 4992: 4959: 4955: 4907: 4901: 4866: 4860: 4847: 4812: 4802: 4790:. Retrieved 4783:the original 4778: 4765: 4730: 4720: 4681: 4675: 4663:. Retrieved 4615: 4605: 4562: 4558: 4548: 4536:. Retrieved 4531: 4518: 4509: 4499: 4456: 4452: 4442: 4425: 4419: 4392: 4388: 4375: 4363:. Retrieved 4358: 4345: 4292: 4288: 4278: 4269: 4256: 4239: 4226: 4214:. Retrieved 4209: 4196: 4169: 4147: 4141: 4116: 4112: 4099: 4087:. Retrieved 4080:the original 4063: 4056: 4011: 4004: 3977: 3971: 3944: 3937: 3888: 3832: 3823: 3798: 3794: 3771: 3762: 3756: 3743: 3733:December 14, 3731:. Retrieved 3727:the original 3717: 3705:. Retrieved 3700: 3691: 3673: 3664: 3654: 3642:. Retrieved 3635:the original 3621: 3612: 3564: 3560: 3554: 3544:November 17, 3542:. Retrieved 3525: 3505: 3498: 3487:the original 3473: 3441:(2): 64–76. 3438: 3434: 3428: 3383: 3373: 3367:: 2592–2599. 3364: 3354: 3333: 3298: 3288: 3243: 3233: 3212: 3167: 3157: 3120: 3041: 3022: 3018: 3008: 2995: 2985: 2976: 2964:. Retrieved 2960: 2950: 2931: 2927: 2917: 2892: 2881: 2865:. Springer. 2862: 2856: 2838: 2799: 2788: 2775: 2765: 2759: 2750: 2744: 2728: 2696:(8): 30–37. 2693: 2689: 2683: 2665: 2646: 2640: 2615: 2611: 2601: 2565: 2551: 2539:. Retrieved 2532:the original 2501: 2447: 2443: 2437: 2401: 2397: 2390:Tuzhilin, A. 2354: 2350: 2344: 2331: 2311: 2298: 2286:. Retrieved 2276: 2256: 2236: 2227: 2218: 2205: 2192: 2179: 2170: 2157: 2132: 2128: 2103:. Retrieved 2070: 2059: 2042: 2038: 2008: 2004: 1998: 1971: 1933: 1929: 1887: 1883: 1877: 1868: 1862: 1835: 1822: 1812:December 31, 1810:. Retrieved 1799: 1764: 1760: 1718: 1705: 1678: 1665: 1652: 1617: 1613: 1603: 1576: 1570: 1539: 1535: 1525: 1513:. Retrieved 1509:the original 1504: 1494: 1467: 1381:Configurator 1336: 1304: 1285: 1272: 1246: 1235: 1229: 1223: 1217: 1205: 1194: 1188: 1178: 1172: 1163: 1157: 1137: 1131: 1116: 1110:October 2023 1107: 1096:Please help 1091:verification 1088: 1035: 1020: 1014: 1003: 997: 991: 975:data privacy 966: 960: 952: 947: 938: 935: 923: 919: 915: 895: 880: 854: 843: 837: 835: 831: 824: 801: 797: 789: 769: 766: 757: 748: 735: 727:Technologies 719: 713: 707: 701: 695: 690: 688:filtering). 683: 681:approaches. 672: 638: 634: 609: 597:user profile 595:To create a 594: 581:user profile 578: 573: 571: 558: 551: 545: 539: 529: 520: 497:Examples of 496: 488: 470: 459: 447: 436: 401: 393:Paul Resnick 374: 367: 359: 352: 344: 320: 309: 270: 268: 252: 242: 238: 234: 230: 226: 222: 220: 60:Star ratings 35: 29: 5301:December 8, 5137:: 439–457. 4665:October 16, 4565:(2): 1–49. 4538:December 2, 4365:November 1, 4216:November 1, 3025:(4): 1–19. 2966:October 31, 2541:October 22, 2450:(1): 5–53. 2288:October 27, 2105:February 2, 2088:. pp. 1436:Rating site 1008:Serendipity 1004:Serendipity 917:imprecise. 887:evaluations 792:smartphones 647:text mining 540:Scalability 404:Adomavicius 389:Pattie Maes 369:Elaine Rich 18:Recommender 5632:Categories 5322:Nieman Lab 5214:2003.07631 5173:2104.13030 4691:2005.09683 4625:1907.06902 4572:1911.07698 3574:1805.02276 3393:1902.05570 3345:2006.05779 3253:1711.04725 3224:1808.09781 3177:1706.03847 3102:1812.02353 3076:1511.06939 1443:References 1366:Cold start 1333:Television 1307:polarizing 1299:See also: 998:Robustness 872:Evaluation 720:Meta-level 657:(see also 592:research. 554:Amazon.com 530:Cold start 523:cold start 414:Approaches 383:, then at 347:cold start 81:Cold start 5109:254475908 5074:232150789 4976:0924-1868 4757:221785064 4660:196831663 4597:208138060 4491:149344712 4483:1369-118X 4305:CiteSeerX 4133:213169978 4016:CiteSeerX 3897:CiteSeerX 3843:CiteSeerX 3801:: 37–45. 3644:April 30, 3443:CiteSeerX 3149:221191348 2698:CiteSeerX 2656:1301.7363 2632:207035184 2474:207731647 2452:CiteSeerX 2428:206742345 2406:CiteSeerX 2149:125187672 2045:: 29–50. 1912:161141937 1904:1058-8167 1636:1573-7721 1339:broadband 1212:black-box 1021:Labelling 979:profiling 953:Diversity 702:Switching 464:forms of 243:algorithm 173:MovieLens 65:Long tail 55:Relevance 5479:July 17, 5452:July 17, 5426:July 17, 5421:Engadget 5396:cite web 5386:July 17, 5360:July 17, 5327:July 17, 5033:15665277 4839:52942462 4792:April 3, 4089:March 5, 3929:18903114 3815:10651930 3681:Archived 3591:19209845 3465:16752808 3420:62903207 3325:50775765 3280:21066930 3050:Archived 2889:(2007). 2846:Archived 2720:58370896 2690:Computer 2371:16544257 2320:Archived 2265:Archived 2245:Archived 1950:59337456 1936:: 1–22. 1783:25186906 1733:ACM Copy 1644:36511631 1349:See also 1321:, which 1063:Bellogín 1051:Ekstrand 696:Weighted 642:metadata 546:Sparsity 462:implicit 306:Overview 287:websites 283:metadata 279:platform 275:software 259:playlist 235:platform 160:Research 42:Concepts 5554:. CUP. 5529:. CUP. 5447:Gizmodo 5381:Poynter 4893:2215419 4430:Bibcode 4411:8996665 4337:1977107 4272:: 1–39. 4248:8307833 4048:8202591 3893:225–231 3707:June 1, 3295:"STAMP" 3204:1159769 2934:: 1–9. 2593:4411601 2528:8202591 2090:253–260 1808:. WIRED 1791:4460749 1515:June 1, 1323:YouTube 1315:Twitter 1256:(SVD), 1252:(LSA), 1055:Konstan 967:Privacy 714:Cascade 685:Netflix 364:History 323:Last.fm 225:, or a 5558: 5533: 5516: 5246: 5107: 5072: 5062: 5031: 5021: 4984:388764 4982: 4974: 4934:333956 4932: 4922: 4891: 4881: 4837: 4827: 4755: 4745: 4708: 4658: 4648: 4595: 4489: 4481: 4409: 4335: 4325: 4307: 4246: 4184: 4131: 4046: 4036: 4018: 3992: 3959: 3927: 3917: 3899: 3863: 3845: 3813: 3589: 3463: 3445: 3418: 3408: 3323: 3313: 3278: 3268: 3202: 3192: 3147: 3137: 2905: 2869: 2830:653908 2828: 2818: 2718: 2700: 2630: 2591: 2581: 2526: 2516: 2472: 2454: 2426: 2408: 2369: 2147: 2096: 1986: 1948: 1910: 1902: 1850: 1789: 1781: 1761:Nature 1725: 1693: 1642: 1634: 1591: 1542:(59). 1482: 1280:PubMed 1146:, and 1043:RecSys 661:) and 628:, and 613:tf–idf 239:engine 231:system 5507:Books 5209:arXiv 5168:arXiv 5105:S2CID 5070:S2CID 5029:S2CID 4980:S2CID 4930:S2CID 4889:S2CID 4835:S2CID 4786:(PDF) 4775:(PDF) 4753:S2CID 4686:arXiv 4656:S2CID 4620:arXiv 4593:S2CID 4567:arXiv 4528:(PDF) 4487:S2CID 4407:S2CID 4385:(PDF) 4355:(PDF) 4333:S2CID 4301:54–62 4266:(PDF) 4244:S2CID 4236:(PDF) 4206:(PDF) 4129:S2CID 4109:(PDF) 4083:(PDF) 4068:(PDF) 4044:S2CID 3925:S2CID 3841:–37. 3811:S2CID 3701:WIRED 3665:Index 3638:(PDF) 3631:(PDF) 3587:S2CID 3569:arXiv 3530:(PDF) 3490:(PDF) 3483:(PDF) 3461:S2CID 3416:S2CID 3388:arXiv 3340:arXiv 3321:S2CID 3276:S2CID 3248:arXiv 3219:arXiv 3200:S2CID 3172:arXiv 3145:S2CID 3097:arXiv 3071:arXiv 2826:S2CID 2804:(PDF) 2716:S2CID 2675:(PDF) 2651:arXiv 2628:S2CID 2589:S2CID 2561:(PDF) 2535:(PDF) 2524:S2CID 2498:(PDF) 2470:S2CID 2424:S2CID 2367:S2CID 2145:S2CID 2079:SIGIR 1946:S2CID 1908:S2CID 1832:(PDF) 1787:S2CID 1715:(PDF) 1675:(PDF) 1640:S2CID 1311:Polis 1015:Trust 770:DRARS 708:Mixed 559:Many 241:, or 5607:2018 5556:ISBN 5531:ISBN 5514:ISBN 5481:2024 5454:2024 5428:2024 5402:link 5388:2024 5362:2024 5329:2024 5303:2014 5277:2015 5244:ISBN 5060:ISBN 5019:ISBN 4972:ISSN 4920:ISBN 4879:ISBN 4825:ISBN 4794:2021 4743:ISBN 4706:ISBN 4667:2019 4646:ISBN 4540:2013 4479:ISSN 4367:2013 4323:ISBN 4218:2013 4182:ISBN 4091:2021 4034:ISBN 3990:ISBN 3957:ISBN 3915:ISBN 3861:ISBN 3735:2011 3709:2015 3646:2009 3546:2011 3406:ISBN 3311:ISBN 3266:ISBN 3190:ISBN 3135:ISBN 2968:2016 2903:ISBN 2867:ISBN 2816:ISBN 2579:ISBN 2543:2013 2514:ISBN 2290:2017 2107:2008 2094:ISBN 1984:ISBN 1900:ISSN 1848:ISBN 1814:2015 1779:PMID 1723:ISBN 1691:ISBN 1632:ISSN 1589:ISBN 1517:2015 1505:TIME 1480:ISBN 1061:and 1059:Said 900:and 808:Lyft 806:and 804:Uber 588:and 408:Beel 385:SICS 325:and 293:and 5219:doi 5205:109 5178:doi 5139:doi 5097:doi 5052:doi 5011:hdl 5003:doi 4964:doi 4912:doi 4871:doi 4817:doi 4735:doi 4696:doi 4638:hdl 4630:doi 4585:hdl 4577:doi 4469:hdl 4461:doi 4397:doi 4315:doi 4174:doi 4121:doi 4072:doi 4026:doi 3982:doi 3949:doi 3907:doi 3853:doi 3803:doi 3579:doi 3538:ACM 3453:doi 3398:doi 3303:doi 3258:doi 3182:doi 3125:doi 3027:doi 2984:", 2936:doi 2932:157 2899:325 2808:doi 2708:doi 2620:doi 2571:doi 2506:doi 2462:doi 2416:doi 2359:doi 2137:doi 2133:112 2086:ACM 2076:ACM 2047:doi 2013:doi 1976:doi 1938:doi 1892:doi 1840:doi 1769:doi 1765:513 1683:doi 1622:doi 1581:doi 1544:doi 1472:doi 1278:or 1206:An 1100:by 929:or 910:DCG 908:or 5634:: 5591:. 5471:. 5445:. 5419:. 5398:}} 5394:{{ 5379:. 5349:. 5337:^ 5320:. 5267:. 5217:. 5203:. 5199:. 5176:. 5164:35 5162:. 5133:. 5129:. 5117:^ 5103:. 5093:25 5091:. 5068:. 5058:. 5027:. 5017:. 5009:. 4978:. 4970:. 4960:26 4958:. 4954:. 4942:^ 4928:. 4918:. 4887:. 4877:. 4833:. 4823:. 4811:. 4777:. 4751:. 4741:. 4729:. 4704:. 4694:. 4654:. 4644:. 4636:. 4628:. 4614:. 4591:. 4583:. 4575:. 4563:39 4561:. 4557:. 4508:. 4485:. 4477:. 4467:. 4457:21 4455:. 4451:. 4405:. 4393:22 4391:. 4387:. 4331:. 4321:. 4313:. 4303:. 4299:: 4291:. 4287:. 4268:. 4238:. 4180:. 4156:^ 4127:. 4117:23 4115:. 4111:. 4042:. 4032:. 4024:. 3988:. 3955:. 3923:. 3913:. 3905:. 3895:. 3887:. 3875:^ 3859:. 3851:. 3839:25 3809:. 3799:19 3797:. 3785:^ 3699:. 3663:. 3611:. 3599:^ 3585:. 3577:. 3565:45 3563:. 3536:: 3515:^ 3459:. 3451:. 3439:26 3437:. 3414:. 3404:. 3396:. 3382:. 3363:. 3319:. 3309:. 3297:. 3274:. 3264:. 3256:. 3242:. 3198:. 3188:. 3180:. 3166:. 3143:. 3133:. 3111:^ 3085:^ 3061:^ 3021:. 3017:. 2959:. 2930:. 2926:. 2901:. 2824:. 2814:. 2797:. 2714:. 2706:. 2694:42 2692:. 2626:. 2616:17 2614:. 2610:. 2587:. 2577:. 2563:. 2522:. 2512:. 2500:. 2483:^ 2468:. 2460:. 2448:22 2446:. 2422:. 2414:. 2402:17 2400:. 2396:. 2380:^ 2365:. 2355:19 2353:. 2143:. 2131:. 2127:. 2115:^ 2092:. 2084:: 2043:20 2041:. 2037:. 2025:^ 2009:24 2007:. 1982:. 1958:^ 1944:. 1934:13 1932:. 1920:^ 1906:. 1898:. 1888:12 1886:. 1846:. 1785:. 1777:. 1763:. 1759:. 1741:^ 1731:. 1689:. 1677:. 1638:. 1630:. 1618:77 1616:. 1612:. 1587:. 1558:^ 1538:. 1534:. 1503:. 1478:. 1450:^ 1142:, 1053:, 1006:– 933:. 665:. 653:, 649:, 624:, 620:, 468:. 445:. 399:. 329:. 289:, 269:A 237:, 221:A 5609:. 5483:. 5456:. 5430:. 5404:) 5390:. 5364:. 5331:. 5305:. 5279:. 5252:. 5227:. 5221:: 5211:: 5184:. 5180:: 5170:: 5147:. 5141:: 5135:7 5111:. 5099:: 5076:. 5054:: 5035:. 5013:: 5005:: 4986:. 4966:: 4936:. 4914:: 4895:. 4873:: 4841:. 4819:: 4796:. 4759:. 4737:: 4714:. 4698:: 4688:: 4669:. 4640:: 4632:: 4622:: 4599:. 4587:: 4579:: 4569:: 4542:. 4493:. 4471:: 4463:: 4436:. 4432:: 4413:. 4399:: 4369:. 4339:. 4317:: 4293:5 4250:. 4220:. 4190:. 4176:: 4135:. 4123:: 4093:. 4074:: 4050:. 4028:: 3998:. 3984:: 3965:. 3951:: 3931:. 3909:: 3869:. 3855:: 3817:. 3805:: 3737:. 3711:. 3667:. 3648:. 3615:. 3593:. 3581:: 3571:: 3548:. 3467:. 3455:: 3422:. 3400:: 3390:: 3348:. 3342:: 3327:. 3305:: 3282:. 3260:: 3250:: 3227:. 3221:: 3206:. 3184:: 3174:: 3151:. 3127:: 3105:. 3099:: 3079:. 3073:: 3035:. 3029:: 3023:6 2970:. 2944:. 2938:: 2911:. 2875:. 2832:. 2810:: 2770:. 2739:, 2737:. 2722:. 2710:: 2659:. 2653:: 2634:. 2622:: 2595:. 2573:: 2545:. 2508:: 2478:. 2476:. 2464:: 2432:. 2430:. 2418:: 2375:. 2373:. 2361:: 2292:. 2151:. 2139:: 2109:. 2053:. 2049:: 2019:. 2015:: 1992:. 1978:: 1952:. 1940:: 1914:. 1894:: 1856:. 1842:: 1816:. 1793:. 1771:: 1735:. 1699:. 1685:: 1646:. 1624:: 1597:. 1583:: 1552:. 1546:: 1540:9 1519:. 1488:. 1474:: 1123:) 1117:( 1112:) 1108:( 1094:. 536:. 210:e 203:t 196:v 20:)

Text is available under the Creative Commons Attribution-ShareAlike License. Additional terms may apply.