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COVER FEATURE MATRIX FACTORIZATION TECHNIQUES FOR ...

Computer 42 COVER FEATUREP ublished by the IEEE Computer Society0018-9162/09/$ 2009 IEEE Such systems are particularly useful for entertainment products such as movies, music, and TV shows. Many cus-tomers 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. Com-panies can analyze this data to recommend movies to particular customers. RecommendeR system stRategiesBroadly 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 at-tributes 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.

content-based techniques, collaborative filtering suffers from what is called the cold start problem, due to its inability to ad-dress the system’s new products and users. In this aspect, content filtering is superior. The two primary areas of collaborative filtering are the neighborhood methods and latent factor models. Neighbor-

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