document: Factorization meets the neighborhood : a multifaceted collaborative filtering model Recommender systems provide users with personalized suggestions for products or services . These systems often rely on Collaborating Filtering ( CF ) , where past transactions are analyzed in order to establish connections between users and products . The two more successful approaches to CF are latent factor models , which directly profile both users and products , and neighborhood models , which analyze similarities between products or users . In this work we introduce some innovations to both approaches . The factor and neighborhood models can now be smoothly merged , thereby building a more accurate combined model . Further accuracy improvements are achieved by extending the models to exploit both explicit and implicit feedback by the users . The methods are tested on the Netflix data . Results are better than those previously published on that dataset . In addition , we suggest a new evaluation metric , which highlights the differences among methods , based on their performance at a top-K recommendation task .
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