Improving user recommendation by extracting social topics and interest topics of users in uni-directional social networks

摘要

With the rapid growth of population on social networks, people are confronted with information overload problem. This clearly makes filtering the targeted users a demanding and key research task. Uni-directional social networks are the scenarios where users provide limited follow or not binary features. Related works prefer to utilize these follower-followee relations for recommendation. However, a major problem of these methods is that they assume every follower-followee user pairs are equally likely, and this leads to the coarse user following preferences inferring. Intuitively, a user’s adoption of others as followees may be motivated by her interests as well as social connections, hence a good recommender should be able to separate the two situations and take both factors into account for better recommendation results. In this regard, we propose a new user recommendation framework namely UIS-MF in this work. UIS-MF can well capture user preferences by involving both interest and social factors in prediction, and targeted to recommend Top-N followees who have similar interest and close social connection relevant to a target user. Specifically, we first present a unified probabilistic topic model on follower-followee relations, namely UIS-LDA, and it employs Generalized Pólya Urn (GPU) models on mutual-following relations for discovering interest topics and social topics of users. Next we propose a community-based method for user recommendation, it organizes social communities and interest communities based on the estimation of topics obtained from UIS-LDA, and then performs Matrix Factorization (MF) method on each community to generate N most likely followees for individual user. Systematic experiments on Twitter, Sina Weibo and Epinions datasets have not only revealed the significant effect of our UIS-LDA model for the extraction of interest and social topics of users in improving recommending accuracy, but also demonstrated the advantage of our proposed recommendation framework over competitive baselines by large margins.