TUEF通过融合内容与社交信息，更精准识别问答平台专家。
Towards Robust Expert Finding in Community Question Answering Platforms
- 融合内容与社交信息，构建主题导向的用户建模方法
- 在StackOverflow数据集上超越现有最优模型表现
- 提升专家识别透明度，适合平台信任机制优化
本文提出TUEF，一种面向主题的用户交互模型，用于社区问答（CQA）平台中的公平专家发现。专家发现任务旨在从社区中识别出能提供准确回答的熟练用户。TUEF通过利用内容信息与社交信息的多样性，提升专家识别的精确性与任务鲁棒性。我们在大型开源数据集StackOverflow上进行了可复现的实验评估，结果表明TUEF在多个指标上持续优于当前最先进的方法，同时提升了专家识别过程的透明性。
原文摘要 · Abstract (English)
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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