Leveraging Topic Specificity and Social Relationships for Expert Finding in Community Question Answering Platforms

TL;DR

This paper introduces TUEF, a topic-oriented user-interaction model that leverages content and social data to improve expert finding in community question answering platforms, demonstrating significant performance gains.

Contribution

The paper presents TUEF, a novel multi-layer graph model that combines content and social information with learning-to-rank techniques for transparent and effective expert identification.

Findings

TUEF outperforms competitors with at least 42.42% improvement in P@1.

Significant performance gains in NDCG@3, R@5, and MRR metrics.

TUEF enhances expert finding especially in large, topic-specific communities.

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. 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.