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Published in: Pattern Analysis and Applications 3/2016

01-08-2016 | Industrial and Commercial Application

A multi-attribute probabilistic matrix factorization model for personalized recommendation

Authors: Feng Tan, Li Li, Zeyu Zhang, Yunlong Guo

Published in: Pattern Analysis and Applications | Issue 3/2016

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Abstract

Recommendation systems can interpret personal preferences and recommend the most relevant choices to the benefit of countless users. Attempts to improve the performance of recommendation systems have hence been the focus of much research in an era of information explosion. As users would like to ask about shopping information with their friend in real life and plentiful information concerning items can help to improve the recommendation accuracy, traditional work on recommending based on users’ social relationships or the content of item tagged by users fails as recommending process relies on mining a user’s historical information as much as possible. This paper proposes a new recommending model incorporating the social relationship and content information of items (SC) based on probabilistic matrix factorization named SC-PMF (Probabilistic Matrix Factorization with Social relationship and Content of items). Meanwhile, we take full advantage of the scalability of probabilistic matrix factorization, which helps to overcome the often encountered problem of data sparsity. Experiments demonstrate that SC-PMF is scalable and outperforms several baselines (PMF, LDA, CTR, SocialMF) for recommending.

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Metadata
Title
A multi-attribute probabilistic matrix factorization model for personalized recommendation
Authors
Feng Tan
Li Li
Zeyu Zhang
Yunlong Guo
Publication date
01-08-2016
Publisher
Springer London
Published in
Pattern Analysis and Applications / Issue 3/2016
Print ISSN: 1433-7541
Electronic ISSN: 1433-755X
DOI
https://doi.org/10.1007/s10044-015-0510-2

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