Towards predicting the best answers in community-based question-answering services

Qiongjie Tian, Peng Zhang, Baoxin Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

35 Scopus citations

Abstract

Community-based question-answering (CQA) services contribute to solving many difficult questions we have. For each question in such services, one best answer can be designated, among all answers, often by the asker. However, many questions on typical CQA sites are left without a best answer even if when good candidates are available. In this paper, we attempt to address the problem of predicting if an answer may be selected as the best answer, based on learning from labeled data. The key tasks include designing features measuring important aspects of an answer and identifying the most importance features. Experiments with a Stack Overflow dataset show that the contextual information among the answers should be the most important factor to consider.

Original languageEnglish (US)
Title of host publicationProceedings of the 7th International Conference on Weblogs and Social Media, ICWSM 2013
PublisherAAAI press
Pages725-728
Number of pages4
StatePublished - 2013
Event7th International AAAI Conference on Weblogs and Social Media, ICWSM 2013 - Cambridge, MA, United States
Duration: Jul 8 2013Jul 11 2013

Other

Other7th International AAAI Conference on Weblogs and Social Media, ICWSM 2013
CountryUnited States
CityCambridge, MA
Period7/8/137/11/13

ASJC Scopus subject areas

  • Media Technology

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  • Cite this

    Tian, Q., Zhang, P., & Li, B. (2013). Towards predicting the best answers in community-based question-answering services. In Proceedings of the 7th International Conference on Weblogs and Social Media, ICWSM 2013 (pp. 725-728). AAAI press.