Formulation of a hybrid expertise retrieval system in community question answering services

In this paper, we propose a hybrid expertise retrieval system for community question answering services. The proposed system consists of two segments: a text based segment and a network based segment. For a given question, the text based segment estimates users’ knowledge introducing two new concepts: question hardness and question answerer association. The network based segment, moreover, incorporates users’ relative performances into the network structure. We denote the outputs of these two segments as knowledge score and authority score, respectively. We aggregate these two scores using a fusion technique to quantify the expertise of a given user for a given question. We have generated four datasets by downloading questions and answers from Yahoo! Answers. The performance of the proposed system is found to be superior than that of 18 state-of-the-art algorithms on these four real-world datasets.