PTEM: A POPULARITY-BASED TOPICAL EXPERTISE MODEL FOR COMMUNITY QUESTION ANSWERING

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Community Question Answering (CQA) websites are widely used in sharing knowledge, where users can ask questions, reply answers and evaluate answers. So far, the evaluation of answers has been explained by the contents of answers through the investigation of users' topics of interest and expertise levels. In this paper we focus on modeling the user's evaluation behavior, in that users can see the answerer's profile as well as the answer content before evaluating the quality of the answer. We propose a model called Popularity-based Topical Expertise Model (PTEM), a generative model to analyze the rich-get-richer phenomenon that popular user's answers are more recommended. We can simultaneously estimate the topical expertise of each user and the strength of the rich-get-richer effect through the EM algorithm combined with collapsed Gibbs sampling. Experiments are performed on the StackExchange data, and the results demonstrate a rich-get-richer phenomenon in the community. We further discuss the superiority and usefulness of the proposed model through analysis in the discipline of philosophy.
Publisher
INST MATHEMATICAL STATISTICS
Issue Date
2020-09
Language
English
Article Type
Article
Citation

ANNALS OF APPLIED STATISTICS, v.14, no.3, pp.1304 - 1325

ISSN
1932-6157
DOI
10.1214/20-AOAS1346
URI
http://hdl.handle.net/10203/276949
Appears in Collection
CS-Journal Papers(저널논문)MA-Journal Papers(저널논문)
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