Joint user knowledge and matrix factorization for recommender systems

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  1. Special Issue on Web Information Systems Engineering

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.

Keywords

Recommender systems Social networks User status Matrix factorization 

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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

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

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