电子学报2025，Vol.53Issue(1)：151-162,12.DOI:10.12263/DZXB.20230387
基于自监督图卷积和注意力机制实现隐式反馈降噪的社交推荐
Denoising Implicit Feedback with Self-Supervised Graph Convolution Network and Attention Mechanism for Social Recommendation
摘要
Abstract
Social recommender systems based on graph neural networks(GNNs)have achieved promising perfor-mance.However,challenges exist in GNN-based social recommendation models,such as the neighborhood aggregation op-eration of GNN-based models amplifying noise in users'implicit behaviors,resulting in suboptimal user and item represen-tations.Additionally,the heterogeneity of edges in the user-item graph and the user social relationship graph leads to user representations learned on two different semantic spaces,where direct fusion also results in suboptimal representations.To address these issues,this paper proposes a social recommendation model based on self-supervised graph convolution and an attention mechanism to achieve implicit feedback noise reduction.The model captures users'true interests from the original user-item graph,generating a denoised user-item interaction graph;a novel method is introduced for fusing user vectors to integrate heterogeneous user vector representations.Experimental results on two public datasets demonstrate that the pro-posed model significantly improves the recommendation performance over the baseline models.Specifically,on the lastfm dataset,the performance improvement ranges from 1.18%to 3.87%,while on the ciao dataset,the improvement ranges from 3.56%to 7.31%.The effectiveness of each module is verified through ablation experiments.关键词
注意力机制/隐式反馈/图卷积神经网络/自监督学习/社交推荐Key words
attention mechanism/implicit feedback/graph convolution neural networks/self-supervised learning/so-cial recommendation分类
信息技术与安全科学引用本文复制引用
郭向星,周魏,杨正益,文俊浩,杨佳佳,刘蔓..基于自监督图卷积和注意力机制实现隐式反馈降噪的社交推荐[J].电子学报,2025,53(1):151-162,12.基金项目
重庆市技术创新与应用发展重大项目(No.CSTB2022TIAD-STX0006) （No.CSTB2022TIAD-STX0006）
国家自然科学基金(No.72074036,No.62072060) Science and Technology Innovation Key Research and Development Program of Chongqing(No.CSTB2022TIAD-STX0006) （No.72074036,No.62072060）
National Natural Science Foundation of China(No.72074036,No.62072060) （No.72074036,No.62072060）
