What We Vote for? Answer Selection from User Expertise View in Community Question Answering

Abstract

Answer selection is an important problem in community question answering (CQA), as it enables the distilling of reliable information and knowledge. Most existing approaches tackle this problem as a text matching task. However, they ignore the influence of the community in voting the best answers. Answer quality is highly correlated with semantic relevance and user expertise in CQA. In this paper, we formalize the answer selection problem from the user expertise view, considering both the semantic relevance in question-answer pair and user expertise in question-user pair. We design a novel matching function, explicitly modeling the influence of user expertise in community acceptance. Moreover, we introduce latent user vectors into the representation learning of answer, capturing the implicit topic interests in learned user vectors. Extensive experiments on two datasets from real world CQA sites demonstrate that our model outperforms state-of-the-art approaches for answer selection in CQA. Furthermore, the user representations learned by our model provide us a quantitative way to understand both the authority and topic-sensitive interests of users.