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.
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