Learning to recommend questions based on public interest

Authors:
Jun Wang;Xia Hu;Zhoujun Li;Wenhan Chao;Biyun Hu
Affiliations:
Beihang University, Beijing, China;Arizona State University, Tempe, USA;Beihang University, Beijing, China;Beihang University, Beijing, China;Beihang University, Beijing, China
Venue:
Proceedings of the 20th ACM international conference on Information and knowledge management
Year:
2011

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Abstract

This paper is concerned with the problem of question recommendation in the setting of Community Question Answering (CQA). Given a question as query, our goal is to rank all of the retrieved questions according to their likelihood of being good recommendations for the query. In this paper, we propose a notion of public interest, and show how public interest can boost the performance of question recommendation. In particular, to model public interest in question recommendation, we build a language model to combine relevance score to the query and popularity score regarding question popularity. Experimental results on Yahoo!Answers dataset demonstrate the performance of question recommendation can be greatly improved with considering the public interest.