NDLI: Factorization meets the neighborhood: a multifaceted collaborative filtering model

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Factorization meets the neighborhood: a multifaceted collaborative filtering model

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Factorization meets the neighborhood: a multifaceted collaborative filtering model

Content Provider	ACM Digital Library
Author	Koren, Yehuda
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.
Starting Page	426
Ending Page	434
Page Count	9
File Format	PDF
ISBN	9781605581934
DOI	10.1145/1401890.1401944
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2008-08-24
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Collaborative filtering Recommender systems
Content Type	Text
Resource Type	Article

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