NDLI: Collaborative filtering with decoupled models for preferences and ratings

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Collaborative filtering with decoupled models for preferences and ratings

Content Provider	ACM Digital Library
Author	Jin, Rong Callan, Jamie Si, Luo Zhai, ChengXiang
Abstract	In this paper, we describe a new model for collaborative filtering. The motivation of this work comes from the fact that two users with very similar preferences on items may have very different rating schemes. For example, one user may tend to assign a higher rating to all items than another user. Unlike previous models of collaborative filtering, which determine the similarity between two users only based on their rating performance, our model treats the user's preferences on items separately from the user's rating scheme. More specifically, for each user, we build two separate models: a preference model capturing which items are favored by the user and a rating model capturing how the user would rate an item given the preference information. The similarity of two users is computed based on the underlying preference model, instead of the surface ratings. We compare the new model with several representative previous approaches on two data sets. Experiment results show that the new model outperforms all the previous approaches that are tested consistently on both data sets.
Starting Page	309
Ending Page	316
Page Count	8
File Format	PDF
ISBN	1581137230
DOI	10.1145/956863.956922
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2003-11-03
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Preference model Collaborative filtering Probabilistic model Application Rating model
Content Type	Text
Resource Type	Article

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