Name
Abstract | ||
|---|---|---|
Stack Overflow and MedHelp are examples of domain-specific community-based question answering (CQA) systems. Different from CQA systems for general topics (e.g., Yahoo! Answers, Baidu Knows), questions and answers in domain-specific CQA systems are mostly in the same topical domain, enabling more comprehensive interaction between users on fine-grained topics. In such systems, users are more likely to ask questions on unfamiliar topics and to answer questions matching their expertise. Users can also vote answers based on their judgements. In this paper, we propose a Tri-Role Topic Model (TRTM) to model the tri-roles of users (i.e., as askers, answerers, and voters, respectively) and the activities of each role including composing question, selecting question to answer, contributing and voting answers. The proposed model can be used to enhance CQA systems from many perspectives. As a case study, we conducted experiments on ranking answers for questions on Stack Overflow, a CQA system for professional and enthusiast programmers. Experimental results show that TRTM is effective in facilitating users getting ideal rankings of answers, particularly for new and less popular questions. Evaluated on nDCG, TRTM outperforms state-of-the-art methods. |
Year | Venue | Keywords |
|---|---|---|
2015 | PROCEEDINGS OF THE TWENTY-NINTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE | question answering,topic model |
Field | DocType | Citations |
Learning to rank,World Wide Web,Ask price,Question answering,Information retrieval,Ranking,Voting,Computer science,Topic model,Stack overflow | Conference | 5 |
PageRank | References | Authors |
0.41 | 13 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zongyang Ma | 1 | 518 | 16.13 |
| Aixin Sun | 2 | 3071 | 156.89 |
| Quan Yuan | 3 | 1131 | 44.03 |
| gao cong | 4 | 4086 | 169.93 |