Paper: Improving Question Recommendation by Exploiting Information Need

ACL ID P11-1143
Title Improving Question Recommendation by Exploiting Information Need
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2011
Authors

In this paper we address the problem of ques- tion recommendation from large archives of community question answering data by ex- ploiting the users’ information needs. Our experimental results indicate that questions based on the same or similar information need can provide excellent question recommenda- tion. We show that translation model can be effectively utilized to predict the information need given only the user’s query question. Ex- periments show that the proposed information need prediction approach can improve the per- formance of question recommendation.