Joint user knowledge and matrix factorization for recommender systems

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.