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Temporal-Weighted Bipartite Graph Model for Sparse Expert Recommendation in Community Question Answering

Published: 19 June 2023 Publication History

Abstract

Community Question Answering (CQA) websites are valuable knowledge repositories where individuals exchange information by asking and answering questions. With an ever-increasing number of questions and high in-flow and out-flow of users in these communities, a key challenge is to design effective strategies for recommending experts for new questions. This requires robust approaches that facilitate modeling users’ expertise given their changing interests and sparse historical data, at the same time being computationally less expensive for periodic updates. In this paper, we propose a simple graph diffusion-based expert recommendation model for CQA, that can outperform state-of-the-art convolutional neural network and transformers-based deep learning representatives and collaborative models. Our proposed method learns users’ expertise in the context of both semantic and temporal information to capture their changing interests and activity levels with time. Experiments on six real-world datasets from the Stack Exchange network demonstrate that our approach outperforms competitive baseline methods. Further, experiments on cold-start users (users with a limited historical record) show our model achieves an average of 50% performance gain compared to the best baseline method.

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Cited By

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  • (2024)“The double-edged sword of inflated help”: Unravelling the motivation crowding in community question-answering platformsPLOS ONE10.1371/journal.pone.029762719:3(e0297627)Online publication date: 13-Mar-2024
  • (2024)A Study of Expert Finding Methods for Multi-Granularity Encoded Community Question Answering by Fusing Graph Neural NetworksIEEE Access10.1109/ACCESS.2024.345054412(142168-142180)Online publication date: 2024
  • (2024)Deep expertise and interest personalized transformer for expert findingInformation Processing & Management10.1016/j.ipm.2024.10377361:5(103773)Online publication date: Sep-2024
  • Show More Cited By

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        cover image ACM Conferences
        UMAP '23: Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization
        June 2023
        333 pages
        ISBN:9781450399326
        DOI:10.1145/3565472
        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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        Published: 19 June 2023

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        Author Tags

        1. community question answering
        2. expert recommendation
        3. network-based inference
        4. temporal dynamics
        5. weighted bipartite graph

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        • SoBigData++: European Integrated Infrastructure for Social Mining and Big Data Analytics

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        Overall Acceptance Rate 162 of 633 submissions, 26%

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        View all
        • (2024)“The double-edged sword of inflated help”: Unravelling the motivation crowding in community question-answering platformsPLOS ONE10.1371/journal.pone.029762719:3(e0297627)Online publication date: 13-Mar-2024
        • (2024)A Study of Expert Finding Methods for Multi-Granularity Encoded Community Question Answering by Fusing Graph Neural NetworksIEEE Access10.1109/ACCESS.2024.345054412(142168-142180)Online publication date: 2024
        • (2024)Deep expertise and interest personalized transformer for expert findingInformation Processing & Management10.1016/j.ipm.2024.10377361:5(103773)Online publication date: Sep-2024
        • (2024)MATERExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121576237:PBOnline publication date: 1-Feb-2024

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