A New Approach to Answerer Recommendation in Community Question Answering Services

Community Question Answering (CQA) service which enables users to ask and answer questions have emerged popular on the web. However, lots of questions usually can't be resolved by appropriate answerers effectively. To address this problem, we present a novel approach to recommend users who are most likely to be able to answer the new question. Differently with previous methods, this approach utilizes the inherent semantic relations among asker-question-answerer simultaneously and perform the Answerer Recommendation task based on tensor factorization. Experimental results on two real-world CQA dataset show that the proposed method is able to recommend appropriate answerers for new questions and outperforms other state-of-the-art approaches.