TY  - CONF
ID  - www2009195
UR  - http://www2009.eprints.org/195/
A1  - Qu, Mingcheng
A1  - Qiu, Guang
A1  - He, Xiaofei
A1  - Zhang, Cheng
A1  - Wu, Hao
A1  - Bu, Jiajun
A1  - Chen, Chun
Y1  - 2009/04//
N2  - 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 ?nd 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.
TI  - Probabilistic Question Recommendation for Question Answering Communities
SP  - 1229
M2  - Madrid, Spain
AV  - public
EP  - 1229
T2  - 18th International World Wide Web Conference
ER  -