Denoising Implicit Feedback with Self-Supervised Graph Convolution Network and Attention Mechanism for Social Recommendation

GUO Xiang-xing; ZHOU Wei; YANG Zheng-yi; WEN Jun-hao; YANG Jia-jia; LIU Man

doi:10.12263/DZXB.20230387

您当前的位置：

首页 >

文章列表页 >

Denoising Implicit Feedback with Self-Supervised Graph Convolution Network and Attention Mechanism for Social Recommendation

PAPERS | 更新时间：2025-12-08

- Denoising Implicit Feedback with Self-Supervised Graph Convolution Network and Attention Mechanism for Social Recommendation
- ACTA ELECTRONICA SINICA Vol. 53, Issue 1, Pages: 151-162(2025)
- 作者机构：
  
  1.重庆大学大数据与软件学院，重庆 400044
  2.信息物理社会可信服务计算教育部重点实验室（重庆大学），重庆 400044
  3.中国移动通信集团重庆有限公司，重庆 401320
- 作者简介：
- 基金信息：
  
  Chongqing Research Program of Basic Research and Frontier Technology(CSTB2022TIAD-STX0006);National Natural Science Foundation of China(72074036;62072060)
- DOI：10.12263/DZXB.20230387
  CLC： TP311;
- Received：28 April 2023，
  
  Revised：2024-07-09，
  
  Published：25 January 2025
- 稿件说明：
移动端阅览
郭向星, 周魏, 杨正益, 等. 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(01): 151-162.

GUO Xiang-xing, ZHOU Wei, YANG Zheng-yi, et al. Denoising Implicit Feedback with Self-Supervised Graph Convolution Network and Attention Mechanism for Social Recommendation[J]. Acta Electronica Sinica, 2025, 53(01): 151-162.
郭向星, 周魏, 杨正益, 等. 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(01): 151-162. DOI：10.12263/DZXB.20230387

GUO Xiang-xing, ZHOU Wei, YANG Zheng-yi, et al. Denoising Implicit Feedback with Self-Supervised Graph Convolution Network and Attention Mechanism for Social Recommendation[J]. Acta Electronica Sinica, 2025, 53(01): 151-162. DOI：10.12263/DZXB.20230387

摘要

基于图神经网络的社交推荐系统取得了较好的性能，然而，基于图神经网络的社交推荐模型存在以下挑战：基于图神经网络的模型的邻域聚集操作会放大用户的隐式行为中的噪声，使得用户和物品的向量表示存在偏差；用户物品图中的边和用户社交关系图中的边的异质性，导致基于图神经网络在两张图上学习到的用户向量表示存在于不同的语义空间，直接融合往往得到次优的向量表示. 针对上述问题，本文提出了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模型. 该模型从原始的用户物品图中捕捉用户的真实兴趣，生成降噪的用户物品交互图；提出一种新颖的用户向量融合方法，对异质的用户向量表示进行融合.在两个公开数据集上的实验结果表明，所提出的模型在不同数据集上的推荐性能均较基线模型有显著提升.在lastfm数据集上，推荐性能提升了1.18%至3.87%；在ciao数据集上，推荐性能提升了3.56%至7.31%.通过消融实验验证了模型各个模块的有效性.

Abstract

Social recommender systems based on graph neural networks (GNNs) have achieved promising performance. However

challenges exist in GNN-based social recommendation models

such as the neighborhood aggregation operation of GNN-based models amplifying noise in users' implicit behaviors

resulting in suboptimal user and item representations. 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 proposed 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

Keywords

references

黄璐 , 林川杰 , 何军 , 等 . 融合主题模型和协同过滤的多样化移动应用推荐 [J ] . 软件学报 , 2017 , 28 ( 3 ): 708 - 720 .

HUANG L , LIN C J , HE J , et al . Diversified mobile app recommendation combining topic model and collaborative filtering [J ] . Journal of Software , 2017 , 28 ( 3 ): 708 - 720 . (in Chinese)

黄立威 , 江碧涛 , 吕守业 , 等 . 基于深度学习的推荐系统研究综述 [J ] . 计算机学报 , 2018 , 41 ( 7 ): 1619 - 1647 .

HUANG L W , JIANG B T , LV S Y , et al . Survey on deep learning based recommender systems [J ] . Chinese Journal of Computers , 2018 , 41 ( 7 ): 1619 - 1647 . (in Chinese)

GAO C , WANG X , HE X N , et al . Graph neural networks for recommender system [C ] // Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining . New York : ACM , 2022 : 1623 - 1625 .

张帅 , 高旻 , 文俊浩 , 等 . 基于自监督学习的去流行度偏差推荐方法 [J ] . 电子学报 , 2022 , 50 ( 10 ): 2361 - 2371 .

ZHANG S , GAO M , WEN J H , et al . Self-supervised learning for alleviating popularity bias in recommender systems [J ] . Acta Electronica Sinica , 2022 , 50 ( 10 ): 2361 - 2371 . (in Chinese)

曹阳 , 高旻 , 余俊良 , 等 . 基于双图混合随机游走的社会化推荐模型 [J ] . 电子学报 , 2023 , 51 ( 2 ): 286 - 296 .

CAO Y , GAO M , YU J L , et al . Bi-graph mix-random walk based social recommendation model [J ] . Acta Electronica Sinica , 2023 , 51 ( 2 ): 286 - 296 . (in Chinese)

TANG J L , HU X , LIU H . Social recommendation: A review [J ] . Social Network Analysis and Mining , 2013 , 3 ( 4 ): 1113 - 1133 .

SHARMA K , LEE Y C , NAMBI S , et al . A survey of graph neural networks for social recommender systems [J ] . ACM Computing Surveys , 2024 , 56 ( 10 ): 1 - 34 .

WU L , SUN P J , FU Y J , et al . A neural influence diffusion model for social recommendation [C ] // Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval . New York : ACM , 2019 : 235 - 244 .

WU L , LI J W , SUN P J , et al . DiffNet: A neural influence and interest diffusion network for social recommendation [J ] . IEEE Transactions on Knowledge and Data Engineering , 2022 , 34 ( 10 ): 4753 - 4766 .

YANG L W , WANG S J , TAO Y Z , et al . DGRec: Graph neural network for recommendation with diversified embedding generation [C ] // Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining . New York : ACM , 2023 : 661 - 669 .

HE X N , DENG K , WANG X , et al . LightGCN: Simplifying and powering graph convolution network for recommendation [C ] // Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval . New York : ACM , 2020 : 639 - 648 .

LIAO J , ZHOU W , LUO F J , et al . SocialLGN: Light graph convolution network for social recommendation [J ] . Information Sciences , 2022 , 589 : 595 - 607 .

KOREN Y . Factorization meets the neighborhood: A multifaceted collaborative filtering model [C ] // Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . New York : ACM , 2008 : 426 - 434 .

陈碧毅 , 黄玲 , 王昌栋 , 等 . 融合显式反馈与隐式反馈的协同过滤推荐算法 [J ] . 软件学报 , 2020 , 31 ( 3 ): 794 - 805 .

CHEN B Y , HUANG L , WANG C D , et al . Explicit and implicit feedback based collaborative filtering algorithm [J ] . Journal of Software , 2020 , 31 ( 3 ): 794 - 805 . (in Chinese)

WANG W J , FENG F L , HE X N , et al . Denoising implicit feedback for recommendation [C ] // Proceedings of the 14th ACM International Conference on Web Search and Data Mining . New York : ACM , 2021 : 373 - 381 .

WEI Y W , WANG X , NIE L Q , et al . Graph-refined convolutional network for multimedia recommendation with implicit feedback [C ] // Proceedings of the 28th ACM International Conference on Multimedia . New York : ACM , 2020 : 3541 - 3549 .

于旭 , 何亚东 , 杜军威 , 等 . 一种结合显式特征和隐式特征的开发者混合推荐算法 [J ] . 软件学报 , 2022 , 33 ( 5 ): 1635 - 1651 .

YU X , HE Y D , DU J W , et al . Developer hybrid recommendation algorithm based on combination of explicit features and implicit features [J ] . Journal of Software , 2022 , 33 ( 5 ): 1635 - 1651 . (in Chinese)

JAISWAL A , BABU A R , ZADEH M Z , et al . A survey on contrastive self-supervised learning [J ] . Technologies , 2021 , 9 ( 1 ): 2 .

WU J C , WANG X , FENG F L , et al . Self-supervised graph learning for recommendation [C ] // Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval . New York : ACM , 2021 : 726 - 735 .

ZHANG Y S , HUANG J J , LI M , et al . Contrastive graph learning for social recommendation [J ] . Frontiers in Physics , 2022 , 10 : 830805 .

LIU Z , MA Y P , OUYANG Y X , et al . Contrastive learning for recommender system [EB/OL ] . ( 2021-01-05 )[ 2023-04-04 ] . https://arxiv.org/abs/2101.01317v1 https://arxiv.org/abs/2101.01317v1 .

RENDLE S , FREUDENTHALER C , GANTNER Z , et al . BPR: Bayesian personalized ranking from implicit feedback [EB/OL ] . ( 2012-05-09 )[ 2023-04-04 ] . https://arxiv.org/abs/1205.2618v1 https://arxiv.org/abs/1205.2618v1 .

CANTADOR I , BRUSILOVSKY P , KUFLIK T , et al . Second workshop on information heterogeneity and fusion in recommender systems (HetRec2011) [C ] // Proceedings of the Fifth ACM Conference on Recommender Systems . New York : ACM , 2011 : 387 - 388 .

LIUFU Y W , SHEN H . Social recommendation via graph attentive aggregation [M ] // Parallel and Distributed Computing, Applications and Technologies . Cham : Springer International Publishing , 2022 : 369 - 382 .

KINGMA D P , BA J , HAMMAD M M . Adam: A method for stochastic optimization [EB/OL ] . ( 2017-01-30 )[ 2023-04-04 ] . https://arxiv.org/abs/1412.6980v9 https://arxiv.org/abs/1412.6980v9 .

ZHU F , CHEN C C , WANG Y , et al . DTCDR: A framework for dual-target cross-domain recommendation [C ] // Proceedings of the 28th ACM International Conference on Information and Knowledge Management . New York : ACM , 2019 : 1533 - 1542 .

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Modulation Recognition Method Based on Improved Res2Net and Adaptive Multi-Scale Window Pooling

Sequence-Based and Cross-Modal Alignment Model for Protein Function Prediction

Leveraging Action Description Generation and Cross-Modal Semantic Alignment for Skeleton-Based Action Recognition

NAHGNN: Neighborhood Aware Heterogeneous Graph Neural Network

RGE-Pipeline: Fast Evaluation for LLM-Based Retrieval-Augmented Generation Systems

Related Author

ZHOU Wei

WEN Jun-hao

LIU Man

WANG Dan

LI Wanjie

JIANG Fengyang

XU Min

HU Chun-ling

Related Institution

China Mobile Group Chongqing Co., Ltd

School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications

School of Artificial Intelligence and Big Data, Hefei University

School of Artificial Intelligence and Computer Science, Shaanxi Normal University

Key Laboratory of Modern Teaching Technology (Ministry of Education)

⁰