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In this paper we address the problem of question recommendation from large archives of community question answering data by exploiting the users’ information needs. Our experimental results indicate that questions based on the same or similar information need can provide excellent question recommendation. We show that translation model can be effectively utilized to predict the information need given only the user’s query question. | Improving Question Recommendation by Exploiting Information Need Shuguang Li Department of Computer Science University of York YO10 5DD UK sgli@cs.york.ac.uk Suresh Manandhar Department of Computer Science UniVersity of York YO10 5DD UK suresh@cs.york.ac.uk Abstract In this paper we address the problem of question recommendation from large archives of community question answering data by exploiting the users information needs. Our experimental results indicate that questions based on the same or similar information need can provide excellent question recommendation. We show that translation model can be effectively utilized to predict the information need given only the user s query question. Experiments show that the proposed information need prediction approach can improve the performance of question recommendation. 1 Introduction There has recently been a rapid growth in the number of community question answering CQA services such as Yahoo Answers1 Askville2 and WikiAnswer3 where people answer questions posted by other users. These CQA services have built up very large archives of questions and their answers. They provide a valuable resource for question answering research. Table 1 is an example from Yahoo Answers web site. In the CQA archives the title part is the user s query question and the user s information need is usually expressed as natural language statements mixed with questions expressing their interests in the question body part. In order to avoid the lag time involved with waiting for a personal response and to enable high quali 1http answers.yahoo.com 2http askville.amazon.com 3http wiki.answers.com 1425 ty answers from the archives to be retrieved we need to search CQA archives of previous questions that are closely associated with answers. If a question is found to be interesting to the user then a previous answer can be provided with very little delay. Question search and question recommendation are proposed to facilitate finding highly .
