research-article

Collaboration-Aware Hybrid Learning for Knowledge Development Prediction

Authors:

Hui XiongAuthors Info & Claims

WWW '24: Proceedings of the ACM Web Conference 2024

Pages 3976 - 3985

https://doi.org/10.1145/3589334.3645326

Published: 13 May 2024 Publication History

Abstract

In recent years, the rise of online Knowledge Management Systems (KMSs) has significantly improved work efficiency in enterprises. Knowledge development prediction, as a critical application within these online platforms, enables organizations to proactively address knowledge gaps and align their learning initiatives with evolving job requirements. However, it still confronts challenges in exploring the influence of collaborative networks on knowledge development and adapting to ecological situations in working environment. To this end, in this paper, we propose a Collaboration-Aware Hybrid Learning approach (CAHL) for predicting the future knowledge acquisition of employees and quantifying the impact of various knowledge learning patterns. Specifically, to fully harness the inherent rules of knowledge development, we first learn the knowledge co-occurrence and prerequisite relationships with an association prompt attention mechanism to generate effective knowledge representations through a specially-designed Job Knowledge Embedding module. Then, we aggregate the features of mastering knowledge and work collaborators for employee representations in another Employee Embedding module. Moreover, we propose to model the process of employee knowledge development via a Hybrid Learning Simulation module that integrates both collaborative learning and self learning to predict future-acquired job knowledge of employees. Finally, extensive experiments conducted on a real-world dataset clearly validate the effectiveness of CAHL.

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Cited By

Qin CFang CYao KChen XZhuang FZhu H(2025)COTR: Efficient Job Task Recognition for Occupational Information Systems with Class-Incremental LearningACM Transactions on Management Information Systems10.1145/3712306Online publication date: 14-Jan-2025
https://doi.org/10.1145/3712306
Lin FZhao ZZhu XZhang DShen SLi XXu TZhang SChen EBaeza-Yates RBonchi F(2024)When Box Meets Graph Neural Network in Tag-aware RecommendationProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671973(1770-1780)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671973
Yu XQin CShen DYang SMa HZhu HZhang XBaeza-Yates RBonchi F(2024)RIGL: A Unified Reciprocal Approach for Tracing the Independent and Group Learning ProcessesProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671711(4047-4058)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671711
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Index Terms

Collaboration-Aware Hybrid Learning for Knowledge Development Prediction
1. Human-centered computing
  1. Collaborative and social computing
    1. Collaborative and social computing theory, concepts and paradigms
      1. Computer supported cooperative work
2. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Conferences

WWW '24: Proceedings of the ACM Web Conference 2024

May 2024

4826 pages

ISBN:9798400701719

DOI:10.1145/3589334

General Chairs:
Tat-Seng Chua
National University of Singapore
,
Chong-Wah Ngo
Singapore Management University
,
Proceedings Chair:
Roy Ka-Wei Lee
Singapore University of Technology and Design
,
Program Chairs:
Ravi Kumar
Google
,
Hady W. Lauw
Singapore Management University

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Published: 13 May 2024

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National Natural Science Foundation of China

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WWW '24

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WWW '24: The ACM Web Conference 2024

May 13 - 17, 2024

Singapore, Singapore

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Cited By

Qin CFang CYao KChen XZhuang FZhu H(2025)COTR: Efficient Job Task Recognition for Occupational Information Systems with Class-Incremental LearningACM Transactions on Management Information Systems10.1145/3712306Online publication date: 14-Jan-2025
https://doi.org/10.1145/3712306
Lin FZhao ZZhu XZhang DShen SLi XXu TZhang SChen EBaeza-Yates RBonchi F(2024)When Box Meets Graph Neural Network in Tag-aware RecommendationProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671973(1770-1780)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671973
Yu XQin CShen DYang SMa HZhu HZhang XBaeza-Yates RBonchi F(2024)RIGL: A Unified Reciprocal Approach for Tracing the Independent and Group Learning ProcessesProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671711(4047-4058)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671711
Wang TDeng LWang CLian JYan YYuan NZhang QXiong HBaeza-Yates RBonchi F(2024)COMET: NFT Price Prediction with Wallet ProfilingProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671621(5893-5904)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671621
Wu WWang CShen DQin CChen LXiong HHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)AFDGCF: Adaptive Feature De-correlation Graph Collaborative Filtering for RecommendationsProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657724(1242-1252)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657724

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