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Recommender systems or recommendation systems (sometimes replacing "system" with a synonym such as platform or engine) are a subclass of information filtering system that seek to predict the 'rating' or 'preference' that a user would give to an item.〔 Recommender systems have become extremely common in recent years, and are applied in a variety of applications. The most popular ones are probably movies, music, news, books, research articles, search queries, social tags, and products in general. However, there are also recommender systems for experts,〔H. Chen, A. G. Ororbia II, C. L. Giles (ExpertSeer: a Keyphrase Based Expert Recommender for Digital Libraries ), in arXiv preprint 2015〕 collaborators,〔H. Chen, L. Gou, X. Zhang, C. Giles (Collabseer: a search engine for collaboration discovery ), in ACM/IEEE Joint Conference on Digital Libraries (JCDL) 2011〕 jokes, restaurants, financial services,〔Alexander Felfernig, Klaus Isak, Kalman Szabo, Peter Zachar, (The VITA Financial Services Sales Support Environment ), in AAAI/IAAI 2007, pp. 1692-1699, Vancouver, Canada, 2007.〕 life insurance, persons (online dating), and Twitter followers.〔Pankaj Gupta, Ashish Goel, Jimmy Lin, Aneesh Sharma, Dong Wang, and Reza Bosagh Zadeh (WTF:The who-to-follow system at Twitter ), Proceedings of the 22nd international conference on World Wide Web〕 == Overview == Recommender systems typically produce a list of recommendations in one of two ways - through collaborative or content-based filtering.〔Hosein Jafarkarimi; A.T.H. Sim and R. Saadatdoost (A Naïve Recommendation Model for Large Databases ), International Journal of Information and Education Technology, June 2012〕 Collaborative filtering approaches building 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.〔Prem Melville and Vikas Sindhwani, (Recommender Systems ), Encyclopedia of Machine Learning, 2010.〕 Content-based filtering approaches utilize a series of discrete characteristics of an item in order to recommend additional items with similar properties.〔 〕 These approaches are often combined (see Hybrid Recommender Systems). The differences between collaborative and content-based filtering can be demonstrated by comparing two popular music recommender systems - Last.fm and Pandora Radio. * 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 other users with similar interests. As this approach leverages the behavior of users, it is an example of a collaborative filtering technique. * Pandora uses the properties of a song or artist (a subset of the 400 attributes provided by the Music Genome Project) in order 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. Each type of system has its own strengths and weaknesses. In the above example, Last.fm requires a large amount of information on a user in order to make accurate recommendations. This is an example of the cold start problem, and is common in collaborative filtering systems.〔 〕 While Pandora needs very little information to get started, it is far more limited in scope (for example, it can only make recommendations that are similar to the original seed). Recommender systems are a useful alternative to search algorithms since they help users discover items they might not have found by themselves. Interestingly enough, recommender systems are often implemented using search engines indexing non-traditional data. Montaner provides the first overview of recommender systems, from an intelligent agents perspective.〔.〕 Adomavicius provides a new overview of recommender systems.〔.〕 Herlocker provides an additional overview of evaluation techniques for recommender systems,〔.〕 and Beel et al. discuss the problems of offline evaluations.〔.〕 Beel et al. also provide a literature survey on research paper recommender systems.〔.〕 Recommender systems are an active research topic in the data mining and machine learning fields. Conferences that address recommender system research include RecSys, SIGIR, and KDD. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Recommender system」の詳細全文を読む スポンサード リンク
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