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Joint user knowledge and matrix factorization for recommender systems

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Abstract

Currently, most of the existing recommendation methods treat social network users equally, which assume that the effect of recommendation on a user is decided by the user’s own preferences and social influence. However, a user’s own knowledge in a field has not been considered. In other words, to what extent does a user accept recommendations in social networks need to consider the user’s own knowledge or expertise in the field. In this paper, we propose a novel matrix factorization recommendation algorithm based on integrating social network information such as trust relationships, rating information of users and users’ own knowledge. Specifically, since we cannot directly measure a user’s knowledge in the field, we first use a user’s status in a social network to indicate a user’s knowledge in a field, and users’ status is inferred from the distributions of users’ ratings and followers across fields or the structure of domain-specific social network. Then, we model the final rating of decision-making as a linear combination of the user’s own preferences, social influence and user’s own knowledge. Experimental results on real world data sets show that our proposed approach generally outperforms the state-of-the-art recommendation algorithms that do not consider the knowledge level difference between the users.

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Notes

  1. http://www.amazon.com

  2. https://www.youtube.com/

  3. http://www.netflix.com

  4. https://www.linkedin.com

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Acknowledgements

The authors would like to acknowledge the support for this work from NSFC (Grant Nos. 61432008, 61503178, 61403208) and NUPTSF (Grant No. NY217114).

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Authors and Affiliations

  1. TongDa College, Nanjing University of Posts and Telecommunications, Nanjing, People’s Republic of China

    Yonghong Yu

  2. State Key Lab for Novel Software Technology, Nanjing University, Nanjing, People’s Republic of China

    Yonghong Yu, Yang Gao & Hao Wang

  3. School of Engineering and Advanced Technology, Massey University, Palmerston North, New Zealand

    Ruili Wang

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  1. Yonghong Yu
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  2. Yang Gao
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  3. Hao Wang
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  4. Ruili Wang
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Correspondence to Yonghong Yu.

Additional information

This article belongs to the Topical Collection: Special Issue on Web Information Systems Engineering

Guest Editors: Wojciech Cellary, Hua Wang, and Yanchun Zhang

This paper is an extension version of the WISE’2016 Long Presentation paper “Joint User Knowledge and Matrix Factorization for Recommender Systems” [40].

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Yu, Y., Gao, Y., Wang, H. et al. Joint user knowledge and matrix factorization for recommender systems. World Wide Web 21, 1141–1163 (2018). https://doi.org/10.1007/s11280-017-0476-7

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  • DOI: https://doi.org/10.1007/s11280-017-0476-7

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