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Joint User Knowledge and Matrix Factorization for Recommender Systems

  • Yonghong YuEmail author
  • Yang Gao
  • Hao Wang
  • Ruili Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10041)

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, we first use a user’s status (in this paper, status refers to the number of followers and the number of ratings one has done) in a social network to indicate a user’s knowledge in a field since we cannot directly measure a user’s knowledge in the field. 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.

Keywords

Recommender systems Social networks User status Matrix factorization 

Notes

Acknowledgments

The authors would like to acknowledge the support for this work from the National Natural Science Foundation of China (Grant Nos. 61432008, 61175042, 61403208, 61503178, 61303049) and the Natural Science Foundation of Jiangsu Province of China (BK20150587).

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.State Key Lab for Novel Software TechnologyNanjing UniversityNanjingPeople’s Republic of China
  2. 2.School of Engineering and Advanced TechnologyMassey UniversityPalmerston NorthNew Zealand

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