Abstract

In community question and answering sites, pairs of questions and their high-quality answers (like best answers selected by askers) can be valuable knowledge available to others. However lots of questions receive multiple answers but askers do not label either one as the accepted or best one even when some replies answer their questions. To solve this problem, high-quality answer prediction or best answer prediction has been one of important topics in social media. These user-generated answers often consist of multiple 'views', each capturing different (albeit related) information (e.g., expertise of the asker, length of the answer, etc.). Such views interact with each other in complex manners that should carry a lot of information for distinguishing a potential best answer from others. Little existing work has exploited such interactions for better prediction. To explicitly model these information, we propose a new learning-to-rank method, ranking support vector machine (RankSVM) with weakly hierarchical lasso in this paper. The evaluation of the approach was done using data from Stack Overflow. Experimental results demonstrate that the proposed approach has superior performance compared with approaches in state-of-the-art.

Original languageEnglish (US)
Title of host publicationProceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages307-314
Number of pages8
ISBN (Electronic)9781509028467
DOIs
StatePublished - Nov 21 2016
Event2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016 - San Francisco, United States
Duration: Aug 18 2016Aug 21 2016

Other

Other2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016
CountryUnited States
CitySan Francisco
Period8/18/168/21/16

Fingerprint

learning
social media
Support vector machines
Labels
ranking
expertise
interaction
evaluation
community
performance

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Sociology and Political Science
  • Communication

Cite this

Tian, Q., & Li, B. (2016). Weakly hierarchical lasso based learning to rank in best answer prediction. In Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016 (pp. 307-314). [7752250] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ASONAM.2016.7752250

Weakly hierarchical lasso based learning to rank in best answer prediction. / Tian, Qiongjie; Li, Baoxin.

Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 307-314 7752250.

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

Tian, Q & Li, B 2016, Weakly hierarchical lasso based learning to rank in best answer prediction. in Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016., 7752250, Institute of Electrical and Electronics Engineers Inc., pp. 307-314, 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016, San Francisco, United States, 8/18/16. https://doi.org/10.1109/ASONAM.2016.7752250
Tian Q, Li B. Weakly hierarchical lasso based learning to rank in best answer prediction. In Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 307-314. 7752250 https://doi.org/10.1109/ASONAM.2016.7752250
Tian, Qiongjie ; Li, Baoxin. / Weakly hierarchical lasso based learning to rank in best answer prediction. Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 307-314
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