Towards Community Answer Selection by Jointly Static and Dynamic User
Expertise Modeling

Answer selection, ranking high-quality answers first is a significant problem for the community question answering sites. The existing approaches usually consider it as a text matching task, and then calculate the quality of answers via their semantic relevance to the given question. They thoroughly ignore the additional influence of factors in the community such as the user expertise. In this paper, we propose an answer selection model by the jointly static and dynamic user expertise modeling. Specifically, we simultaneously consider the social influence and the personal interest that affect the user expertise from different views. We propose an inductive strategy to aggregate the social influence from neighbors; we introduce the explicit topic interest of users and capture the context-based personal interest by weighing the activation of each topic. Moreover, we construct two real-world datasets containing rich user information. Extensive experiments on two datasets demonstrate that our model outperforms several state-of-the-art models.