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Graph Neural Networks for Recommender System

Published: 15 February 2022 Publication History

Abstract

Recently, graph neural network (GNN) has become the new state-of-the-art approach in many recommendation problems, with its strong ability to handle structured data and to explore high-order information. However, as the recommendation tasks are diverse and various in the real world, it is quite challenging to design proper GNN methods for specific problems. In this tutorial, we focus on the critical challenges of GNN-based recommendation and the potential solutions. Specifically, we start from an extensive background of recommender systems and graph neural networks. Then we fully discuss why GNNs are required in recommender systems and the four parts of challenges, including graph construction, network design, optimization, and computation efficiency. Then, we discuss how to address these challenges by elaborating on the recent advances of GNN-based recommendation models, with a systematic taxonomy from four critical perspectives: stages, scenarios, objectives, and applications. Last, we finalize this tutorial with conclusions and discuss important future directions.

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    cover image ACM Conferences
    WSDM '22: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining
    February 2022
    1690 pages
    ISBN:9781450391320
    DOI:10.1145/3488560
    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 ACM 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: 15 February 2022

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    Author Tags

    1. graph neural network
    2. recommender system

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    • (2025)Application of Reinforcement Learning Methods Combining Graph Neural Networks and Self-Attention Mechanisms in Supply Chain Route OptimizationSensors10.3390/s2503095525:3(955)Online publication date: 5-Feb-2025
    • (2025)Pone-GNN: Integrating Positive and Negative Feedback in Graph Neural Networks for Recommender SystemsACM Transactions on Recommender Systems10.1145/3711666Online publication date: 4-Jan-2025
    • (2025)Delayed Bottlenecking: Alleviating Forgetting in Pre-trained Graph Neural NetworksIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.351619237:3(1140-1153)Online publication date: Mar-2025
    • (2025)GCN-based prediction method for coal spontaneous combustion temperatureProcess Safety and Environmental Protection10.1016/j.psep.2025.106855196(106855)Online publication date: Apr-2025
    • (2025)Self-supervised category-enhanced graph neural networks for recommendationKnowledge-Based Systems10.1016/j.knosys.2025.113109311(113109)Online publication date: Feb-2025
    • (2025)A self-supervised graph convolutional model for recommendation with exponential moving averageNeural Computing and Applications10.1007/s00521-024-10933-5Online publication date: 24-Jan-2025
    • (2025)Alleviating Dimensional Collapse Problem in Deep Recommender Models by Designing Uniformity LayersDatabase Systems for Advanced Applications10.1007/978-981-97-5555-4_10(148-163)Online publication date: 12-Jan-2025
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