Abstract
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. We present TUEF, a
Topic-oriented User-Interaction model for EF
, which aims to fully and transparently leverage the heterogeneous information available within online CQA platforms. TUEF integrates content and social data by constructing a multi-layer graph that maps user relationships based on their answering patterns on specific topics. By combining these sources of information, TUEF identifies the most relevant 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 across six CQA communities show that TUEF outperforms all competitors, achieving 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.