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. Categories and Subject Descriptors H.3.3 [Information Search and Retrieval]: information filtering General Terms Algorithms, Design, Experimentation Keywords Question Recommendation, Question Answering, PLSA

• Type: Conference
• Year: 2009
• Where: WWW
• Authors: Mingcheng Qu, Guang Qiu, Xiaofei He, Cheng Zhang, Hao Wu, Jiajun Bu, Chun Chen
• Keywords: Internet Technology, Keywords Question Recommendation, Recommendation Approach, User-Interactive Question, WWW 2009