Predicting Best Answerers for New Questions in Community Question Answering
Community question answering (CQA) has become a very popular web service to provide a platform for people to share knowledge. In current CQA services, askers post their questions to the system and wait for answerers to an swer them passively. This procedure leads to several drawbacks. Since new ques tions are presented to all users in the system, the askers can not expect some experts to answer their questions. Meanwhile, answerers have to visit many ques tions and then pick out only a small part of them to answer. To overcome those drawbacks, a probabilistic framework is proposed to predict best answerers for new questions. By tracking answerers answering history, interests of answerers are modeled with the mixture of the Language Model and the Latent Dirichlet Allocation model. User activity and authority information is also taken into con sideration. Experimental results show the proposed method can effectively push new questions to the best answerers.
Community Question Answering Language Model LDA
Mingrong Liu Yicen Liu Qing Yang
National Laboratory of Pattern Recognition Institute of Automation, Chinese Academy of Sciences 95 Zhongguancun East Road, Beijing 100190, China
国际会议
11th International Conference,WAIM 2010(第十一届网络时代管理国际会议)
九寨沟
英文
127-138
2010-07-14（万方平台首次上网日期，不代表论文的发表时间）