cikm Paper cqarank:jointly Model Topics and Expertise in Community Question answering

Introduction: This paper studies how to analyze the user topic interest and professional degree in the question and answer community, and presents a statistical chart model topics Expertise models for this problem.

Paper Source: cikm ' 13.

English Abstract:  community Question Answering (CQA) websites, where people share expertise on open platforms, with become large Repositories of valuable knowledge. To bring the best value out of these knowledge repositories, it's critically important for CQA services to know how to fi nd the right experts, retrieve archived similar questions and recommend best answers to new questions. To tackle the cluster of closely related problems in a principled approach, we proposed Topic Expertise Model (TEM), a No Vel probabilistic generative model with GMM hybrid, to jointly model topics and expertise by integrating textual content m Odel and link structure analysis. Based on TEM results, we proposed cqarank to measure user interests and expertise score under different topics. Leveraging the question answering history based in long-term community reviews and voting, our method could find experts W ITH both similar topical preference and high topical expertise. Experiments carried out on Stack Overflow Data, the largest CQA focused on computer programming, show this method achieves significant improvement over Existin G methods on multiple metrics.

Download Link: http://dl.acm.org/citation.cfm?id=2505720

cikm Paper cqarank:jointly Model Topics and Expertise in Community Question answering