Towards Robust Expert Finding in Community Question Answering Platforms

• URL: http://arxiv.org/abs/2503.02674v1
• Date: Tue, 04 Mar 2025 14:46:01 GMT
• Title: Towards Robust Expert Finding in Community Question Answering Platforms
• Authors: Maddalena Amendola, Andrea Passarella, Raffaele Perego,
• Abstract summary: TUEF is a topic-oriented user-interaction model for fair Expert Finding in Community Question Answering platforms.<n>We exploit diverse types of information, specifically, content and social information, to identify more precisely experts.
• Score: 5.723916517485655
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper introduces TUEF, a topic-oriented user-interaction model for fair Expert Finding in Community Question Answering (CQA) platforms. The Expert Finding task in CQA platforms involves identifying proficient users capable of providing accurate answers to questions from the community. To this aim, TUEF improves the robustness and credibility of the CQA platform through a more precise Expert Finding component. The key idea of TUEF is to exploit diverse types of information, specifically, content and social information, to identify more precisely experts thus improving the robustness of the task. We assess TUEF through reproducible experiments conducted on a large-scale dataset from StackOverflow. The results consistently demonstrate that TUEF outperforms state-of-the-art competitors while promoting transparent expert identification.

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