Items by Zhang, Cheng
Number of items: 1. Qu, Mingcheng and Qiu, Guang and He, Xiaofei and Zhang, Cheng and Wu, Hao and Bu, Jiajun and Chen, Chun 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.
This list was generated on Fri Feb 15 08:55:16 2019 GMT.
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