Topic Community Based Temporal Expertise for Question Routing

TL;DR

This paper introduces a dynamic topic community-based approach for question routing in community Q&A sites, improving recommendation accuracy by addressing data sparsity, computational costs, and site dynamism.

Contribution

It proposes a novel dynamic modeling method based on user activity within topic communities, overcoming limitations of existing static and content-heavy models.

Findings

Significantly outperforms baseline models in experiments

Effective in handling data sparsity issues

Reduces computational costs for updates

Abstract

Question Routing in Community-based Question Answering 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