Question routing via activity-weighted modularity-enhanced factorization

• Published in: Social network analysis and mining Vol. 12; no. 1; p. 155
• Main Authors: Krishna, Vaibhav, Vasiliauskaite, Vaiva, Antulov-Fantulin, Nino
• Format: Journal Article
• Language: English
• Published: Vienna Springer Vienna 01.12.2022; Springer Nature B.V
• Subjects: Answers; Applications of Graph Theory and Complex Networks; Classification; Computer Science; Data Mining and Knowledge Discovery; Dynamic models; Economics; Experts; Game Theory; Humanities; Law; Methodology of the Social Sciences; Modelling; Modularity; Original Article; Questions; Social and Behav. Sciences; Statistics for Social Sciences; Subject specialists; User generated content; Question routing; Community detection; Expert recommendation systems; Social network analysis
• ISSN: 1869-5450; 1869-5469
• DOI: 10.1007/s13278-022-00978-6

Question Routing (QR) in Community-based Question Answering (CQA) websites aims at recommending newly posted questions to potential users who are most likely to provide “accepted answers”. Most of the existing approaches predict users’ expertise based on their past question answering behavior and the content of new questions. However, these approaches suffer from challenges in three aspects: (1) sparsity of users’ past records results in lack of personalized recommendation that at times does not match users’ interest or domain expertise, (2) modeling based on all questions and answers content makes periodic updates computationally expensive, and (3) while CQA sites are highly dynamic, they are mostly considered as static. This paper proposes a novel approach to QR that addresses the above challenges. It is based on dynamic modeling of users’ activity on topic communities. Experimental results on three real-world datasets demonstrate that the proposed model significantly outperforms competitive baseline models.