User-Service Rating Prediction by Exploring Social Users’ Rating Behaviors

Abstract— 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 behaviors. In order to predict user-service ratings, we focus on users’ rating behaviors. In our opinion, the rating behavior 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 behavior diffuses among his/her social friends. Therefore, we
propose a concept of the rating schedule to represent users’ daily rating behaviors. In addition, we propose the factor of interpersonal rating behavior diffusion to deep understand users’ rating behaviors. < final year projects >