Time-aware Social Recommendation Based on User Feedback

Context information such as time, social relationship and user feedback information can be exploited to improve the quality of recommendation. However, most collaborative filtering based methods ignore this kind of information in social recommendation. In this paper, we propose a time-aware social recommendation method based on user feedback for top-k item recommendation in social networks. Our method incorporates the temporal factors by introducing a time weight function, which models the decay of user interest. Moreover, our method considers the user positive feedback and negative feedback information, as well as the social relationship information for recommendation. Empirical analysis and experiments are conducted in Sina Weibo, one of the most popular social network sites in China. The experimental results demonstrate that our method outperforms the collaborative filtering method in terms of MAP for top-k item recommendation.