A novel matrix factorization recommendation algorithm fusing social Trust and Behaviors in micro-blogs

摘要

With the advent of social networks, micro-blogs have become increasingly popular and recommender systems have been widely used to provide personalized services for better user experience. Traditional collaborative filtering is one of the most popular approaches but it suffers with two well-known problems: cold start and data sparsity. Trust relationships and interaction behaviors in social networks can be used to find user's potential preferences. In this paper, we focus on the problem of followee recommendation in micro-blogs and we propose TBSVD, a social Trust and Behavior based Singular Value Decomposition algorithm. First, implicit trust is calculated based on user interaction behaviors including mention, comment and retweet while explicit trust is based on the direct connections between users; Then an extended trust matrix is constructed combining both implicit trust and explicit trust. Finally, we utilize both the extended trust and ratings and apply matrix factorization techniques to build the model. Experiments on KDD Cup 2012 dataset demonstrates that our approach TBSVD achieves better performance in terms of RMSE and MSE.