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MATRIX FACTORIZATION TECHNIQUES FOR RECOMMENDER SYSTEMS (2009)
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| Venue: | IEEE COMPUTER |
| Citations: | 544 - 4 self |
BibTeX
@MISC{Koren09matrixfactorization,
author = {Yehuda Koren and Robert Bell and Chris Volinsky},
title = {MATRIX FACTORIZATION TECHNIQUES FOR RECOMMENDER SYSTEMS},
year = {2009}
}
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Abstract
As the Netflix Prize competition has demonstrated, matrix factorization models are superior to classic nearest-neighbor techniques for producing product recommendations, allowing the incorporation of additional information such as implicit feedback, temporal effects, and confidence levels. Modern consumers are inundated with choices. Electronic retailers and content providers offer a huge selection of products, with unprecedented opportunities to meet a variety of special needs and tastes. Matching consumers with the most appropriate products is key to enhancing user satisfaction and loyalty. Therefore, more retailers have become interested in recommender systems, which analyze patterns of user interest in products to provide personalized recommendations that suit a user’s taste. Because good personalized recommendations can add another dimension to the user experience, e-commerce leaders like Amazon.com and Netflix have made recommender systems a salient part of their websites. Such systems are particularly useful for entertainment products such as movies, music, and TV shows. Many customers will view the same movie, and each customer is likely to view numerous different movies. Customers have proven willing to indicate their level of satisfaction with particular movies, so a huge volume of data is available about which movies appeal to which customers. Companies can analyze this data to recommend movies to particular customers. Recommender system strategies Broadly speaking, recommender systems are based on one of two strategies. The content filtering approach creates a profile for each user or product to characterize its nature. For example, a movie profile could include attributes regarding its genre, the participating actors, its box office popularity, and so forth. User profiles might include demographic information or answers provided on a suitable questionnaire. The profiles allow programs to associate users with matching products. Of course, content-based strategies require gathering external information that might not be available or easy to collect. A known successful realization of content filtering is the Music Genome Project, which is used for the Internet radio service Pandora.com. A trained music analyst scores
Keyphrases
matrix factorization technique recommender system recommender system particular customer e-commerce leader product recommendation user interest matching product classic nearest-neighbor technique matrix factorization model appropriate product numerous different movie netflix prize competition unprecedented opportunity user taste recommender system strategy gathering external information entertainment product content-based strategy personalized recommendation trained music analyst score tv show known successful realization content provider particular movie demographic information music genome project modern consumer user profile many customer implicit feedback user satisfaction salient part confidence level suitable questionnaire huge selection good personalized recommendation temporal effect content filtering movie profile user experience internet radio service pandora huge volume box office popularity special need electronic retailer additional information
