Collaborative filtering recommendation method based on graph convolutional neural networks

Zhengwu Yuan; Xiling Zhan; Yatao Zhou; Hao Yang

doi:10.1117/12.3014407

9 January 2024 Collaborative filtering recommendation method based on graph convolutional neural networks

Zhengwu Yuan, Xiling Zhan, Yatao Zhou, Hao Yang

Proceedings Volume 12969, International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023); 129691U (2024) https://doi.org/10.1117/12.3014407
Event: International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023), 2023, Qingdao, China

Abstract

In the rapidly advancing information technology era, information overload poses a significant challenge. Recommender systems offer a partial solution, yet traditional methods grapple with issues like sparse data and accuracy. For this reason, this paper introduces a novel approach—a high-order graph convolutional collaborative filtering model. This model employs a subgraph generation module to enhance the importance of neighbor nodes during high-order graph convolutions. Our approach yields enhanced embeddings by embedding user-item interaction information using graph techniques, stacking multi-layer graph convolutional networks to capture complex interactions, and leveraging both initial and convoluted embeddings. This paper introduces a constraint loss function to address over-smoothing in graph-based recommendations. Our method's effectiveness is confirmed through extensive experiments on three real-world datasets

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhengwu Yuan, Xiling Zhan, Yatao Zhou, and Hao Yang "Collaborative filtering recommendation method based on graph convolutional neural networks", Proc. SPIE 12969, International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023), 129691U (9 January 2024); https://doi.org/10.1117/12.3014407

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