research-article

Incorporating a Triple Graph Neural Network with Multiple Implicit Feedback for Social Recommendation

Authors:

Liang WangAuthors Info & Claims

ACM Transactions on the Web, Volume 18, Issue 2

Article No.: 23, Pages 1 - 26

https://doi.org/10.1145/3580517

Published: 08 January 2024 Publication History

Abstract

Graph neural networks have been clearly proven to be powerful in recommendation tasks since they can capture high-order user-item interactions and integrate them with rich attributes. However, they are still limited by the cold-start problem and data sparsity. Using social relationships to assist recommendation is an effective practice, but it can only moderately alleviate these problems. In addition, rich attributes are often unavailable, which prevents graph neural networks from being fully effective. Hence, we propose to enrich the model by mining multiple implicit feedback and constructing a triple GCN component. We have noticed that users may be influenced not only by their trusted friends but also by the ratings that already exist. The implicit influence spreads among the item’s previous and potential raters, and makes a difference on future ratings. The implicit influence is analyzed on the mechanism of information propagation, and fused with the user’s binary implicit attitude, since negative influence propagates as well as the positive one. Furthermore, we leverage explicit feedback, social relationships, and multiple implicit feedback in the triple GCN component. Abundant experiments on real-world datasets reveal that our model has improved significantly in the rating prediction task compared with other state-of-the-art methods.

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Index Terms

Incorporating a Triple Graph Neural Network with Multiple Implicit Feedback for Social Recommendation
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
  2. Information systems applications
    1. Data mining
      1. Collaborative filtering

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cover image ACM Transactions on the Web

ACM Transactions on the Web Volume 18, Issue 2

May 2024

378 pages

EISSN:1559-114X

DOI:10.1145/3613666

Editor:
White Ryen
Microsoft Research, USA

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Publication History

Published: 08 January 2024

Online AM: 21 January 2023

Accepted: 03 December 2022

Revised: 27 October 2022

Received: 31 January 2022

Published in TWEB Volume 18, Issue 2

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National Natural Science Foundation of China
Beijing Nova Program from Beijing Municipal Science & Technology Commission

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Lin ZYang JLian YChen YHuang ZNing K(2025)Optimization design of cross border intelligent marketing management model based on multi layer perceptron-grey wolf optimization convolutional neural networkScientific Reports10.1038/s41598-025-89534-815:1Online publication date: 12-Feb-2025
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