Learning for Expertise Matching with Declination Prediction

11/14/2016

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by   Yujie Qian, et al.

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We study the problem of finding appropriate experts who are able to complete timely reviews and would not say "no" to the invitation. The problem is a central issue in many question-and-answer systems, but has received little research attention. Different from most existing studies that focus on expertise matching, we want to further predict the expert's response: given a question, how can we find the expert who is able to provide a quality review and will agree to do it. We formalize the problem as a ranking problem. We first present an embedding-based question-to-expert distance metric for expertise matching and propose a ranking factor graph (RankFG) model to predict expert response. For online evaluation, we developed a Chrome Extension for reviewer recommendation and deployed it in the Google Chrome Web Store, and then collected the reviewers' feedback. We also used the review bidding of a CS conference for evaluation. In the experiments, the proposed method demonstrates its superiority (+6.6-21.2