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A multi-attribute probabilistic matrix factorization model for personalized recommendation

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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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Notes

  1. http://www.amazon.com/.

  2. http://www.ebay.cn/.

  3. http://www.kde.cs.uni-kassel.de/bibsonomy/dumps.

  4. http://www.datatang.com/data/45466.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (No. 61170192). The authors would like to thank anonymous reviewers for their valuable suggestions and comments.

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Authors and Affiliations

  1. Department of Computer Science (Building 25), Southwest University, Chongqing, 400715, People’s Republic of China

    Feng Tan, Li Li, Zeyu Zhang & Yunlong Guo

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  1. Feng Tan
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  2. Li Li
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  3. Zeyu Zhang
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  4. Yunlong Guo
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Correspondence to Li Li.

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Tan, F., Li, L., Zhang, Z. et al. A multi-attribute probabilistic matrix factorization model for personalized recommendation. Pattern Anal Applic 19, 857–866 (2016). https://doi.org/10.1007/s10044-015-0510-2

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