Factorization meets the neighborhood: a multifaceted collaborative filtering model

Authors:
Yehuda Koren
Affiliations:
AT&T, Florham Park, NJ, USA
Venue:
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Year:
2008

Citing 15
Cited 249

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Abstract

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.