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Adaptive self-propagation graph convolutional network for recommendation

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Abstract

Graph Convolutional Networks (GCNs) have received a lot of attention in recommender systems due to their powerful representation learning ability on graph data. Depending on whether to combine ego embeddings after aggregating neighbor embeddings, GCNs can be divided into two categories: without self-propagation and with self-propagation. (1) The GCNs without self-propagation bring the loss of inherent information (e.g., income, age) because of discarding ego embeddings. (2) The existing GCNs with self-propagation treat all nodes (i.e., users and items) indistinguishably, so that the distinctive and diverse characteristics of users and items are overlooked. In light of these problems existed in two types of GCNs, we propose a novel GCN model, Adaptive Self-propagation Graph Convolutional Network (ASP-GCN), to retain inherent information and distinctive characteristics of users and items simultaneously. We first conduct pilot experiments to prove that existing GCNs actually suffer from the aforementioned problems. Then, we use Gumbel-Softmax trick to generate categorical distributions for each node in each layer between two types of embeddings: neighborhood embeddings and hybrid embeddings. Neighborhood embeddings are the aggregation of neighbor embeddings and hybrid embeddings consist of ego and neighborhood embeddings. Next, user and item embeddings are updated by aggregating these two types of embeddings proportionally according to corresponding categorical distributions. After obtaining node embeddings in the last convolution layer, the Bayesian Personalized Ranking loss optimized with a similarity term is used to refine the model parameters. Comprehensive experiments are conducted on three benchmark datasets to demonstrate the effectiveness of ASP-GCN over present state-of-the-art approaches.

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All datasets used in this paper are open datasets.

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Acknowledgements

We acknowledge the editorial committee’s support and all anonymous reviewers for their insightful comments and suggestions, which can improve the content and presentation of this manuscript.

Funding

This work was supported in part by the National Natural Science Foundation of China under Grants 71774159 and 62272066, China Postdoctoral Science Foundation under Grants 2021T140707, Jiangsu Postdoctoral Science Foundation under Grants 2021K565C, and State Key Laboratory of NBC Protection for Civilian under grant SKLNBC2020-23.

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Authors and Affiliations

  1. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 221000, China

    Zhuo Cai, Guan Yuan & Xiaobao Zhuang

  2. School of Computer, Central South University, Changsha, 410083, China

    Senzhang Wang

  3. School of Software Engineering, Chengdu University of Information Technology, Chengdu, 610225, China

    Shaojie Qiao

  4. State Key Laboratory of NBC Protection for Civilian, Beijing, 100000, China

    Mu Zhu

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  1. Zhuo Cai
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Contributions

Zhuo Cai wrote the main manuscript text and designed the experiments, Guan Yuan provided the main idea of this manuscript and organized the full text, Xiaobao Zhuang prepared Figures 1-4 and formated the references. Senzhang Wang checked the English grammar and smoothed the representation of main text, Shaojie Qiao gave many suggestions and instructions in experiments, and Mu Zhu sorted out experimental data. All authors have proofread and approved the final manuscript.

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Correspondence to Guan Yuan.

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Cai, Z., Yuan, G., Zhuang, X. et al. Adaptive self-propagation graph convolutional network for recommendation. World Wide Web 26, 3183–3206 (2023). https://doi.org/10.1007/s11280-023-01182-y

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