A User Personality-Similarity Model for Personalized Followee Recommendation in SINA Microblog

Abstract

Followee recommendation plays an important role in information sharing over microblogging platforms. But as the popularity of microblogging sites increases, the difficulty of deciding who to follow also increases. To solve this problem, in this paper, we propose a User Personality-Similarity (UPS) model for followee recommendation, a novel personality followee recommendation scheme over microblogging systems based on user attributes and the big-five personality model. It quantitatively analyses the effects of user personality in followee selection by combining personality traits with the most commonly user-based predictive factors in microblog. We conduct comprehensive experiments on a large-scale dataset collected from Sina Weibo, the most popular mircoblogging system in China. The results show that our scheme greatly outperforms existing schemes in terms of precision and an accurate appreciation of this model tied to a quantitative analysis of personality is crucial for potential followees selection, and thus, enhance recommendation.