A new temporal and social PMF-based method to predict users' interests in micro-blogging

Abstract

Micro-blogging is becoming an increasingly popular social media platform where users can discover interesting information about the real world and especially corporations are able to understand customers' demands. The fast diffusion of information and the convenience of micro-blogging have resulted in large audiences sharing their daily activities, exchanging opinions and establishing friendships with others. By analyzing the user-generated contents, one can explore users' potential interests, which helps micro-blogging provide users with better personalized information services. Users' behaviors are affected by opinions of their friends and changes in their interests over time. Based on these intuitions, in this paper we propose a temporal and social probabilistic matrix factorization model to predict users' potential interests in micro-blogging. By exploiting the matrix factorization technique to learn latent features of users and topics, our model analyzes the impacts of time information and users' activities, including posting of tweets and establishing friendships with others, on the latent feature space of users and topics of their interests. The proposed model provides a unified way to fuse the time information and the social network structure to predict users' future interests accurately. The experimental results on Sina-weibo, one of the most popular micro-blogging sites in China, demonstrate the efficiency and effectiveness of our proposed model.