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2024 | OriginalPaper | Buchkapitel

Towards Robust Expert Finding in Community Question Answering Platforms

verfasst von : Maddalena Amendola, Andrea Passarella, Raffaele Perego

Erschienen in: Advances in Information Retrieval

Verlag: Springer Nature Switzerland

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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.

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Vorheriges Kapitel A Conversational Robot for Children’s Access to a Cultural Heritage Multimedia Archive

Nächstes Kapitel QuantPlorer: Exploration of Quantities in Text

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Titel: Towards Robust Expert Finding in Community Question Answering Platforms
verfasst von: Maddalena Amendola
Andrea Passarella
Raffaele Perego
Verlag: Springer Nature Switzerland
Buch: Advances in Information Retrieval
Print ISBN: 978-3-031-56068-2

Electronic ISBN: 978-3-031-56069-9

Copyright-Jahr: 2024
DOI: https://doi.org/10.1007/978-3-031-56069-9_12

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