Online Community Question Answering (CQA) platforms have become indispensable tools for users seeking expert solutions to their technical queries. The effectiveness of these platforms relies on their ability to identify and direct questions to the most knowledgeable users within the community, a process known as Expert Finding (EF). EF accuracy is crucial for increasing user engagement and the reliability of provided answers. Despite recent advancements in EF methodologies, blending the diverse information sources available on CQA platforms for effective expert identification remains challenging. In this paper, we present TUEF, a Topic-oriented User-Interaction model for Expert Finding, which aims to fully and transparently leverage the heterogeneous information available within online question-answering communities. TUEF integrates content and social data by constructing a multi-layer graph that maps out user relationships based on their answering patterns on specific topics. By combining these sources of information, TUEF identifies the most relevant and knowledgeable users for any given question and ranks them using learning-to-rank techniques. Our findings indicate that TUEF's topic-oriented model significantly enhances performance, particularly in large communities discussing well-defined topics. Additionally, we show that the interpretable learning-to-rank algorithm integrated into TUEF offers transparency and explainability with minimal performance trade-offs. The exhaustive experiments conducted on six different CQA communities of Stack Exchange show that TUEF outperforms all competitors with a minimum performance boost of 42.42% in P@1, 32.73% in NDCG@3, 21.76% in R@5, and 29.81% in MRR, excelling in both the evaluation approaches present in the previous literature.

翻译：在线社区问答平台已成为用户寻求技术问题专家解决方案不可或缺的工具。这些平台的有效性依赖于其在社区内识别并将问题定向给最具知识用户的能力，这一过程被称为专家发现。专家发现的准确性对于提升用户参与度和所提供答案的可靠性至关重要。尽管专家发现方法近期取得了进展，但融合社区问答平台上可用的多样化信息源以实现有效的专家识别仍具挑战性。本文提出TUEF，一种面向主题的用户交互专家发现模型，旨在全面且透明地利用在线问答社区中可用的异构信息。TUEF通过构建多层图来整合内容与社会数据，该图基于用户在特定主题上的回答模式映射用户关系。通过结合这些信息源，TUEF能够识别出针对任何给定问题最相关且最具知识的用户，并利用排序学习技术对其进行排序。我们的研究结果表明，TUEF的面向主题模型显著提升了性能，尤其是在讨论明确定义主题的大型社区中。此外，我们证明集成于TUEF中的可解释排序学习算法能以极小的性能代价提供透明度和可解释性。在Stack Exchange六个不同社区问答平台上进行的详尽实验表明，TUEF在所有评估指标上均优于所有竞争对手，在P@1上最低提升42.42%，在NDCG@3上提升32.73%，在R@5上提升21.76%，在MRR上提升29.81%，全面超越了现有文献中的评估方法。