Optimization Approach for Question Routing in Community Question Answering Services

Abstract

Community Question Answering (CQA) has increasingly become an important service for people asking questions and providing answers online. Recently, with accumulation of users and contents, much concern has arisen over the efficiency and answer quality and also over technical answering but not generalized. To address this problem, question routing has been proposed which aims at routing new technical questions to suitable answerers, who have both high possibility and high ability to answer the questions. The system will formulate question routing as a multi-objective ranking problem, and present a multi-objective learning-to-rank approach for question routing (MLQR), which can optimize the answering possibility and answer quality of routed users. In MLQR, realizing that questions are usually attached with tags, the system will first propose a tagword topic model (TTM) to derive topical representations of questions. It can be captured at both platform level and thread level. System extend a state-of-the-art learning-to-rank algorithm for training a multi-objective ranking model. Real-world datasets are used. The proposed system will allow one-to-one communication through E-mails. New Pop-up-blocks related to recent searches of users. User interactive display. And finally profile generation of user (eg:- How many questions are answered by the same user on different sites