Expertise Modeling and Recommendation in Online Question and Answer Forums

Question and answer forums provide a method of connecting users and resources that can leverage both the static and dynamic (live) capabilities of a network of human users. We present a recommender for selecting the most appropriate responders given a question. The goal of this work is to encourage expert participation in QA forums by reducing the time investment needed by an expert to find a suitable question, decrease responder load, and to increase questioner confidence in the responses of others. The two primary contributions of this work are: 1. a generative model for characterizing the production of content in an online question and answer forum and 2. a decision theoretic framework for recommending expert participants while maintaining questioner satisfaction and distributing responder load. We have also developed two new metrics tailored to QA forums: responder load and questioner satisfaction. These metrics are used to evaluate the performance of our recommender system on datasets harvested from Yahoo! Answers. Experiments across several topic domains demonstrate our systems ability to predict responder identities and suggest new responders that are likely to have the appropriate expertise.