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.
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