Graph Neural Collaborative Filtering Algorithm Based on Self-Supervised Learning and Degree Centrality
Authors: 
Hao Wang, Chunlong YaoAuthors Info & Claims
Hao Wang, Chunlong YaoAuthors Info & ClaimsPages 59 - 64
Abstract
In recent years, with the introduction of graph neural networks in recommendation systems, collaborative filtering has been significantly improved, especially in handling large-scale, high-dimensional, and sparse user behavior data. Graph neural networks can better capture the complex relationships between users and items. However, existing graph neural network methods still have limitations in dealing with collaborative filtering problems, particularly in addressing data sparsity and long-tail issues. To address these challenges, this paper proposes a novel graph neural collaborative filtering (SDGCF) algorithm, which combines self-supervised learning and degree centrality fusion strategy, as well as jointly optimizes multiple loss functions including BPR_Loss and contrastive loss, to further improve the performance of collaborative filtering algorithms. Through experiments on three public datasets, the SDGCF algorithm demonstrates superior performance in terms of recommendation accuracy, recall, hit rate, and NDCG, proving its effectiveness in alleviating data sparsity and long-tail issues.
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- Graph Neural Collaborative Filtering Algorithm Based on Self-Supervised Learning and Degree Centrality
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Published In
July 2023
115 pages
ISBN:9798400707520
DOI:10.1145/3613330
Copyright © 2023 ACM.
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Association for Computing Machinery
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Publication History
Published: 28 September 2023
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ICDLT 2023
ICDLT 2023: 2023 7th International Conference on Deep Learning Technologies
July 27 - 29, 2023
Dalian, China
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