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research-article

Embedding-based team formation for community question answering

Authors:

Roohollah Etemadi,

Morteza Zihayat,

Ebrahim BagheriAuthors Info & Claims

Volume 623, Issue C

Pages 671 - 692

https://doi.org/10.1016/j.ins.2022.09.036

Published: 01 April 2023 Publication History

Highlights

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Neural embedding based team formation for community question answering systems.

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Finding teams of experts to jointly answer questions on question answering systems.

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A subgraph embedding technique to learn latent representations of teams.

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Retrieve teams of experts with complementary skill sets and high collaboration level.

Abstract

Finding a qualified individual who can independently answer a question on a community question answering platform is becoming more challenging due to the increasing multidisciplinary nature of posted questions. As such, finding a group of experts to collaboratively answer the questions is of paramount importance. To this end, we propose a novel approach to form teams of experts who can collectively answer new questions. The proposed approach, called team2box, learns neural embedding representations based on the content of the posted questions, experts’ engagement with these questions, and past expert collaboration history in order to form a team to answer the posted question. It embeds experts and questions as points and existing teams as regions within the embedding space. Therefore, team2box forms a team whose members (1) collectively cover the knowledge required to answer a question, (2) have successful past experience in jointly answering similar questions, and (3) can work efficiently together to answer the question. Extensive experiments on real-life datasets from Stack Exchange show that team2box outperforms the state-of-the-art by discovering teams with on average 38.97% more covering the skills required to answer new questions and employing experts with collectively a high expertise level.

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Cited By

Etemadi RZihayat MFeng KAdelman JZarrinkalam FBagheri ESakai TIshita EOhshima HRadboud FMao JJose J(2024)It Takes a Team to Triumph: Collaborative Expert Finding in Community QA NetworksProceedings of the 2024 Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region10.1145/3673791.3698404(164-174)Online publication date: 8-Dec-2024
https://dl.acm.org/doi/10.1145/3673791.3698404
Sadegh Zahedi MRahgozar MAghaeizadeh Zoroofi R(2024)MATERExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121576237:PBOnline publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.121576

Index Terms

Embedding-based team formation for community question answering

Index terms have been assigned to the content through auto-classification.

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Published In

cover image Information Sciences: an International Journal

Information Sciences: an International Journal Volume 623, Issue C

Apr 2023

932 pages

ISSN:0020-0255

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Elsevier Inc.

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Elsevier Science Inc.

United States

Publication History

Published: 01 April 2023

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Cited By

Etemadi RZihayat MFeng KAdelman JZarrinkalam FBagheri ESakai TIshita EOhshima HRadboud FMao JJose J(2024)It Takes a Team to Triumph: Collaborative Expert Finding in Community QA NetworksProceedings of the 2024 Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region10.1145/3673791.3698404(164-174)Online publication date: 8-Dec-2024
https://dl.acm.org/doi/10.1145/3673791.3698404
Sadegh Zahedi MRahgozar MAghaeizadeh Zoroofi R(2024)MATERExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121576237:PBOnline publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.121576

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