  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.
