research-article

Temporal Context-Aware Representation Learning for Question Routing

Authors:

Haifeng ChenAuthors Info & Claims

WSDM '20: Proceedings of the 13th International Conference on Web Search and Data Mining

Pages 753 - 761

https://doi.org/10.1145/3336191.3371847

Published: 22 January 2020 Publication History

Abstract

Question routing (QR) aims at recommending newly posted questions to the potential answerers who are most likely to answer the questions. The existing approaches that learn users' expertise from their past question-answering activities usually suffer from challenges in two aspects: 1) multi-faceted expertise and 2) temporal dynamics in the answering behavior. This paper proposes a novel temporal context-aware model in multiple granularities of temporal dynamics that concurrently address the above challenges. Specifically, the temporal context-aware attention characterizes the answerer's multi-faceted expertise in terms of the questions' semantic and temporal information simultaneously. Moreover, the design of the multi-shift and multi-resolution module enables our model to handle temporal impact on different time granularities. Extensive experiments on six datasets from different domains demonstrate that the proposed model significantly outperforms competitive baseline models.

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Cited By

Peng QWang WLiu HHuo CShao MLarson K(2024)Graph collaborative expert finding with contrastive learningProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/253(2288-2296)Online publication date: 3-Aug-2024
https://dl.acm.org/doi/10.24963/ijcai.2024/253
Peng QXu HWang YLiu HHuo CWang W(2024)PEPT: Expert Finding Meets Personalized Pre-TrainingACM Transactions on Information Systems10.1145/369038043:1(1-26)Online publication date: 28-Aug-2024
https://dl.acm.org/doi/10.1145/3690380
Wu LWang RSu LLi J(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
https://doi.org/10.1109/ACCESS.2024.3450544
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Index Terms

Temporal Context-Aware Representation Learning for Question Routing
1. Information systems

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cover image ACM Conferences

WSDM '20: Proceedings of the 13th International Conference on Web Search and Data Mining

January 2020

950 pages

ISBN:9781450368223

DOI:10.1145/3336191

General Chairs:
James Caverlee
Texas A&M University
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Xia "Ben" Hu
Texas A&M University
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Program Chairs:
Mounia Lalmas
Spotify
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Wei Wang
University of California, Los Angeles

Copyright © 2020 ACM.

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Published: 22 January 2020

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WSDM '20: The Thirteenth ACM International Conference on Web Search and Data Mining

February 3 - 7, 2020

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Cited By

Peng QWang WLiu HHuo CShao MLarson K(2024)Graph collaborative expert finding with contrastive learningProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/253(2288-2296)Online publication date: 3-Aug-2024
https://dl.acm.org/doi/10.24963/ijcai.2024/253
Peng QXu HWang YLiu HHuo CWang W(2024)PEPT: Expert Finding Meets Personalized Pre-TrainingACM Transactions on Information Systems10.1145/369038043:1(1-26)Online publication date: 28-Aug-2024
https://dl.acm.org/doi/10.1145/3690380
Wu LWang RSu LLi J(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
https://doi.org/10.1109/ACCESS.2024.3450544
Sadegh Zahedi MRahgozar MAghaeizadeh Zoroofi R(2024)MATER: Bi-level matching-aggregation model for time-aware expert recommendationExpert Systems with Applications10.1016/j.eswa.2023.121576237(121576)Online publication date: Mar-2024
https://doi.org/10.1016/j.eswa.2023.121576
Fallahnejad ZKarimian MLashkari FBeigy H(2024)T-shaped expert mining: a novel approach based on skill translation and focal lossJournal of Intelligent Information Systems10.1007/s10844-023-00831-y62:2(535-554)Online publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1007/s10844-023-00831-y
Islakoglu DChekol MVelegrakis Y(2024)Leveraging Pre-trained Language Models for Time Interval Prediction in Text-Enhanced Temporal Knowledge GraphsThe Semantic Web10.1007/978-3-031-60626-7_4(59-78)Online publication date: 19-May-2024
https://doi.org/10.1007/978-3-031-60626-7_4
Amendola MPassarella APerego R(2024)Towards Robust Expert Finding in Community Question Answering PlatformsAdvances in Information Retrieval10.1007/978-3-031-56069-9_12(152-168)Online publication date: 24-Mar-2024
https://dl.acm.org/doi/10.1007/978-3-031-56069-9_12
Krishna VAntulov-Fantulin N(2023)Temporal-Weighted Bipartite Graph Model for Sparse Expert Recommendation in Community Question AnsweringProceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization10.1145/3565472.3592957(156-163)Online publication date: 18-Jun-2023
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Rostami PShakery A(2023)A deep learning-based expert finding method to retrieve agile software teams from CQAsInformation Processing & Management10.1016/j.ipm.2022.10314460:2(103144)Online publication date: Mar-2023
https://doi.org/10.1016/j.ipm.2022.103144
Etemadi RZihayat MFeng KAdelman JBagheri E(2023)Embedding-based team formation for community question answeringInformation Sciences10.1016/j.ins.2022.09.036623(671-692)Online publication date: Apr-2023
https://doi.org/10.1016/j.ins.2022.09.036
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