Estimating domain-specific user expertise for answer retrieval in community question-answering platforms

Wern Han Lim, Mark James Carman, Sze Meng Jojo Wong

    Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

    2 Citations (Scopus)

    Abstract

    Community Question-Answering (CQA) platforms leverage the inherent wisdom of the crowd { enabling users to retrieve quality information from domain experts through natural language. An important and challenging task is to identify reliable and trusted experts on large popular CQA platforms. State-of-the-art graph-based approaches to expertise estimation consider only user-user interactions without taking the relative contribution of individual answers into account, while pairwise-comparison approaches consider only pairs involving the best-answerer of each question. This research argues that there is a need to account for the user's relative contribution towards solving the question when estimating user expertise and proposes a content-agnostic measure of user contributions. This addition is incorporated into a competition-based approach for ranking users' question answering ability. The paper analyses how improvements in user expertise estimation impact on applications in expert search and answer quality prediction. Experiments using the Yahoo! Chiebukuro data show encouraging performance improvements and robustness over state-of-the-art approaches.

    Original languageEnglish
    Title of host publicationProceedings of the 21st Australasian Document Computing Symposium
    Subtitle of host publicationCaulfield, Victoria, Australia, December 6-7, 2016
    EditorsSarvnaz Karimi, Mark Carman
    PublisherAssociation for Computing Machinery (ACM)
    Pages33-40
    Number of pages8
    ISBN (Electronic)9781450348652
    DOIs
    Publication statusPublished - 5 Dec 2016
    EventAustralasian Document Computing Symposium 2016 - Caulfield, Australia
    Duration: 6 Dec 20167 Dec 2016
    Conference number: 21st
    https://dl.acm.org/doi/proceedings/10.1145/3015022 (Proceedings)

    Conference

    ConferenceAustralasian Document Computing Symposium 2016
    Abbreviated titleADCS 2016
    CountryAustralia
    CityCaulfield
    Period6/12/167/12/16
    Internet address

    Keywords

    • Answer quality
    • Community Question-Answering (CQA)
    • Knowledge mining
    • Pairwise comparison
    • User expertise

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