A Method of User Recommendation in Social Networks Based on Trust Relationship and Topic Similarity

In the research area of user recommendation in social network sites (SNS), there exist problems that some algorithms based on the structure of SNS are resulting in low quality recommendation results due to lack of model and mechanism to express users’ topic similarity, some algorithms which use topic model to measure the theme similarity between users cost a lot of time because of the topic model have a high time complexity in case of large amounts of data. This paper proposed a hybrid method for user recommendation based on trust relationship and topic similarity between users, aiming to widening their circle of friends and enhancing user stickiness of SNS. Two main steps are involved in this process: (1) a trust-propagation based community detection method is proposed to model the users’ social relationship; (2) a topic model is applied to retrieve users’ topics from their microblogging, and gain the recommendations by the topic similarity. Our research brings two major contributions to the research community: (1) a Peer-to-Peer trust model, PGP, is introduced to the field of community detection and we improve the PGP model to compute trust value more precise; (2) a distributed implementation of the topic model is proposed to reduce total execution time. Finally, we conduct experiments with Sina-microblog datasets, which shows the model we proposed can availably compute the trust degree between users, and gain a better result of recommendation. Our evaluation demonstrates the effectiveness, efficiency, and scalability of the proposed method.