Finding experts in community question answering services: A theme based query likelihood language approach

Community question answering services provide an open platform for users to acquire and share their knowledge. In the last decade, popularity of such services has increased noticeably. Large number of unanswered questions is a major problem for the growth of such services. A common way to address this issue is to route a new question to some selected users who have the potentiality in answering the question. Expert finding is the process of selecting such potential answerers. In this article, we have introduced an efficient method for expert finding using the theme in query likelihood language (QLL) model. Theme of a query is nothing but its subject matter and we have decided it based on the parts of speech (POS) of the words in the query. Depending on the theme of the given question, its similarity to a question in the archive is determined using the QLL model. Aggregating the similarity values of the questions a user answered previously (i.e., in the archive), his/her expertise for the given question is obtained. The performance of the proposed method is verified on a real world dataset (obtained from Yahoo! Answers) and it is found to be quite encouraging.