Description

With the boom of social media, it is a very popular trend for people to share what they are doing with friends across various social networking platforms. Nowadays, we have a vast Amount of descriptions, comments, and ratings for local services. The information is valuable for new users to judge whether the services meet their requirements before partaking. In this paper, we propose a user-service rating prediction approach by exploring social users’ rating behaviours. In order to predict user-service ratings, we focus on users’ rating behaviours. In our opinion, the rating behaviour in recommender system could be embodied in these aspects: 1) when user rated the item, 2) what the rating is, 3) what the item is, 4) what the user interest that we could dig from his/her rating records is, and 5) how the user’s rating behaviour diffuses among his/her social friends. Therefore, we propose a concept of the rating schedule to represent users’ daily rating behaviours. In addition, we propose the factor of interpersonal rating behaviour diffusion to deep understand users’ rating behaviours. In the proposed user-service rating prediction approach, we fuse four factors—user personal interest (related to user and the item’s topics), interpersonal interest similarity (related to user interest), interpersonal rating behaviour similarity (related to users’ rating behaviour habits), and interpersonal rating behaviour diffusion (related to users’ behaviour diffusions)—into a unified matrix-factorized framework. We conduct a series of experiments in the Yelp dataset and Douban Movie dataset. Experimental results show the effectiveness of our approach