Personalized recommendation for new questions in community question answering

Abstract

Community question answering(CQA) websites such as Yahoo! Answers and Stack Overflow provide a new way of asking and answering questions which are not well served by general web search engines. Due to the huge volume and ever-increasing number of questions, not all new questions can get fully answered in required time. Therefore, it is of great significance to design some effective strategies of recommending experts for new questions. In this paper, we propose a novel personalized recommendation method for routing new questions to a group of experts. Different from prior work which only considers the topic modeling or the link structure, we aim at recommending new questions to more appropriate experts by considering both of these two factors. Moreover, we design a new strategy of network construction with the personalization fully considered. The comparison experiments are conducted with Stack Overflow data and the experimental results demonstrate that the proposed method improves the recommendation performance over other methods in expert recommendation.