基于自监督图掩码神经网络的社交推荐模型

doi:10.16451/j.cnki.issn1003-6059.202310007

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Abstract The existing self-supervised social recommendation models mostly construct self-supervised signals through the strategies of manual heuristic graph enhancement and contrasts between single-relational views. Thus, the performance of the model is easily affected by the quality of enhanced self-supervised signals, making it challenging to adaptively suppress noise. To solve these problems, a social recommendation model based on self-supervised graph masked neural networks(SGMN) is proposed. Firstly, single-relational views for user-social interaction and item classification are constructed respectively, as well as high-order connected heterogeneous graphs. Specifically, the graph masked learning paradigm is adopted to guide adaptive and learnable data augmentation for the user-social graph. Secondly, a heterogeneous graph encoder is designed to learn the latent semantics of the views, and cross-view contrastive learning is performed on user and item embeddings to complete self-supervised tasks. Then, weighted fusion is conducted on the user and item embeddings separately for recommendation task. Finally, a multi-task training strategy is employed to jointly optimize self-supervised learning, recommendation and graph masked tasks. Experiments on three real datasets demonstrate a certain performance improvement of SGMN.

Key words： Social Recommendation Graph Masked Learning Graph Neural Network Self-Supervised Learning

Received: 25 September 2023

ZTFLH:

TP391

Fund:National Natural Science Foundation of China(No.61972182)

Corresponding Authors: XIA Hongbin, Ph.D., professor. His research interests include personalized recommendation and natural language processing.

About author:: ZANG Xiubo, master student. His research interests include recommendation system and deep learning.LIU Yuan, Ph.D., professor. His research interests include network security and social network.

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	ZANG Xiubo
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Cite this article:

ZANG Xiubo,XIA Hongbin,LIU Yuan. Social Recommendation Model Based on Self-Supervised Graph Masked Neural Networks[J]. Pattern Recognition and Artificial Intelligence, 2023, 36(10): 942-952.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202310007 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2023/V36/I10/942

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