# Towards Robust Expert Finding in Community Question Answering Platforms

Source: [Towards Robust Expert Finding in Community Question Answering Platforms](<https://www.sota2.com/research/paper/68fa77481e209131cea7237c>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T04:14:52.638Z

Authors: Maddalena Amendola, Andrea Passarella, Raffaele Perego

Published: 2025-03-04T14:46:01.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2503.02674>)

[PDF](<https://arxiv.org/pdf/2503.02674.pdf>)