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An Empirical Comparison of Collaborative Filtering Approaches on Netflix Data

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An Empirical Comparison of Collaborative Filtering Approaches on Netflix Data
Recommender systems are widely used in E-Commerce for making automatic suggestions of new items that could meet the interest of a given user. Collaborative Filtering approaches compute recommendations by assuming that users, who have shown similar behavior in the past, will share a common behavior in the future. According to this assumption, the most effective collaborative filtering techniques try to discover groups of similar users in order to infer the preferences of the group members. The purpose of this work is to show an empirical comparison of the main collaborative filtering approaches, namely Baseline, Nearest Neighbors, Latent Factor and Probabilistic models, focusing on their strengths and weaknesses. Data used for the analysis are a sample of the well-known Netflix Prize database. Categories and Subject Descriptors H.2.8 [Database Application]: Data Mining Keywords Recommender Systems, Collaborative Filtering, Netflix
Nicola Barbieri, Massimo Guarascio, Ettore Ritacco
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2010
Where IIR
Authors Nicola Barbieri, Massimo Guarascio, Ettore Ritacco
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