document: Probabilistic question recommendation for question answering communities User-Interactive Question Answering ( QA ) communities such as Yahoo ! Answers are growing in popularity . However , as these QA sites always have thousands of new questions posted daily , it is difficult for users to find the questions that are of interest to them . Consequently , this may delay the answering of the new questions . This gives rise to question recommendation techniques that help users locate interesting questions . In this paper , we adopt the Probabilistic Latent Semantic Analysis ( PLSA ) model for question recommendation and propose a novel metric to evaluate the performance of our approach . The experimental results show our recommendation approach is effective .
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