社交影响增强的图神经网络推荐方法

doi:10.16451/j.cnki.issn1003-6059.202403003

摘要
图/表
参考文献
相关文章 (15)

全文: PDF (705 KB) HTML (1 KB)
输出: BibTeX | EndNote (RIS)

摘要随着在线社交平台的发展,社交推荐已成为推荐系统中的一个重要任务.然而,用户间的社交关系通常具有稀疏性,这在一定程度上限制推荐系统的性能.为此,文中提出社交影响增强的图神经网络推荐方法,旨在利用用户之间的隐式社交关系增强社交推荐的效果.首先,分析用户与物品之间的交互数据,揭示隐含的社交关系,重构用户间的社交图.在此基础上,利用互信息最大化方法,有效融合社交图的全局特征与用户的局部特征.同时,将可学习机制融入图注意力网络中,充分捕获用户和物品间的交互信息.最后,提出一种改进的贝叶斯个性化排序损失,为评分预测任务提供准确的用户特征表示和物品特征表示.在3个公开数据集上的实验表明,文中方法性能较优.

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	代星月
	叶海良
	曹飞龙

关键词 ：图神经网络, 表示学习, 互信息最大化, 社交推荐

Abstract：With the rapid development of online social platforms, social recommendation becomes a critical task in recommender systems. However, the performance of recommendation systems is limited to some extent due to the sparsity of social relationships between users. Therefore, a graph neural network recommendation method based on enhanced social influence is proposed in the paper, aiming to utilize implicit social relationships between users to enhance social recommendation. The implicit social relationships are revealed, and the social graph among users is reconstructed by analyzing interaction information between users and items. On this basis, global features of the social graph are integrated with local features of users effectively via the mutual information maximization method. A learnable mechanism is integrated into the graph attention network to fully capture the interaction information between users and items. An improved Bayesian personalized ranking loss is designed to provide more accurate user and item feature representations for the rating prediction task. Extensive experiments on three public social recommendation datasets demonstrate the superiority of the proposed method.

Key words： Graph Neural Network Representation Learning Mutual Information Maximization Social Recommendation

收稿日期: 2023-12-16

ZTFLH:

TP 181

基金资助:国家自然科学基金项目(No.62176244)资助

通讯作者: 曹飞龙,博士,教授,主要研究方向为深度学习、神经网络及其应用等.E-mail:icteam@163.com.

作者简介: 代星月,硕士研究生,主要研究方向为深度学习、推荐系统.E-mail:xydai199135@163.com. 叶海良,博士,讲师,主要研究方向为深度学习、图神经网络.E-mail:yhl575@163.com.

引用本文:

代星月, 叶海良, 曹飞龙. 社交影响增强的图神经网络推荐方法[J]. 模式识别与人工智能, 2024, 37(3): 221-230. DAI Xingyue, YE Hailiang, CAO Feilong. Graph Neural Network Recommendation Based on Enhanced Social Influence. Pattern Recognition and Artificial Intelligence, 2024, 37(3): 221-230.

链接本文:

http://manu46.magtech.com.cn/Jweb_prai/CN/10.16451/j.cnki.issn1003-6059.202403003 或 http://manu46.magtech.com.cn/Jweb_prai/CN/Y2024/V37/I3/221

[1] CHEN H X, YIN H Z, CHEN T, et al. Social Boosted Recommendation with Folded Bipartite Network Embedding. IEEE Transactions on Knowledge and Data Engineering, 2022, 34(2): 914-926.
[2] GUO Z W, YU K P, LI Y, et al. Deep Learning-Embedded Social Internet of Things for Ambiguity-Aware Social Recommendation. IEEE Transactions on Network Science and Engineering, 2022, 9(3): 1067-1081.
[3] LI N, GAO C, JIN D P, et al. Disentangled Modeling of Social Homophily and Influence for Social Recommendation. IEEE Transactions on Knowledge and Data Engineering, 2023, 35(6): 5738-5751.
[4] 王玙,高琳.基于社交圈的在线社交网络朋友推荐算法.计算机学报, 2014, 37(4): 801-808.
(WANG Y, GAO L.Social Circle-Based Algorithm for Friend Re-commendation in Online Social Networks. Chinese Journal of Computers, 2014, 37(4): 801-808.)
[5] MCPHERSON M, SMITH-LOVIN L, COOK J M.Birds of a Fea-ther: Homophily in Social Networks. Annual Review of Sociology, 2001, 27: 415-444.
[6] MARSDEN P V, FRIEDKIN N E.Network Studies of Social Influence. Sociological Methods Research, 1993, 22(1): 127-151.
[7] 冷亚军,陆青,梁昌勇.协同过滤推荐技术综述.模式识别与人工智能, 2014, 27(8): 720-734.
(LENG Y J, LU Q, LIANG C Y.Survey of Recommendation Based on Collaborative Filtering. Pattern Recognition and Artificial Intelligence, 2014, 27(8): 720-734.)
[8] MA H, YANG H X, LYU M R, et al. SoRec: Social Recommendation Using Probabilistic Matrix Factorization // Proc of the 17th ACM International Conference on Information and Knowledge Ma-nagement. New York,USA: ACM, 2008: 931-940.
[9] YANG B, LEI Y, LIU J M, et al. Social Collaborative Filtering by Trust. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(8): 1633-1647.
[10] JAMALI M, ESTER M.A Matrix Factorization Technique with Trust Propagation for Recommendation in Social Networks // Proc of the 4th ACM Conference on Recommender Systems. New York,USA: ACM, 2010: 135-142.
[11] MA H, ZHOU D Y, LIU C, et al. Recommender Systems with Social Regularization // Proc of the 4th ACM International Confe-rence on Web Search and Data Mining. New York,USA: ACM, 2011: 287-296.
[12] WU L, SUN P J, FU R J, et al. A Neural Influence Diffusion Model for Social Recommendation // Proc of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. New York,USA: ACM, 2019: 235-244.
[13] CHEN C, ZHANG M, WANG C Y, et al. An Efficient Adaptive Transfer Neural Network for Social-Aware Recommendation // Proc of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. New York,USA: ACM, 2019: 225-234.
[14] FAN W Q, LI Q, CHENG M.Deep Modeling of Social Relations for Recommendation. Proceedings of the AAAI Conference on Artificial Intelligence, 2018, 32(1): 8075-8076.
[15] SALAKHUTDINOV R, MNIH A.Probabilistic Matrix Factorization // Proc of the 20th International Conference on Neural Information Processing Systems. Cambridge, USA: MIT Press, 2007: 1257-1264.
[16] ZHAO Z, YANG Q F, LU H Q, et al. Social-Aware Movie Re-commendation via Multimodal Network Learning. IEEE Transactions on Multimedia, 2018, 20(2): 430-440.
[17] WANG X, HE X N, WANG M, et al. Neural Graph Collaborative Filtering // Proc of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. New York,USA: ACM, 2019: 165-174.
[18] FAN W Q, MA Y, LI Q, et al. Graph Neural Networks for Social Recommendation // Proc of the World Wide Web Conference. New York,USA: ACM, 2019: 417-426.
[19] FAN W Q, MA Y, LI Q, et al. A Graph Neural Network Framework for Social Recommendations. IEEE Transactions on Know-ledge and Data Engineering, 2022, 34(5): 2033-2047.
[20] WU L, LI J W, SUN P J, et al. DiffNet++: A Neural Influence and Interest Diffusion Network for Social Recommendation. IEEE Transactions on Knowledge and Data Engineering, 2022, 34(10): 4753-4766.
[21] YANG L W, LIU Z W, DOU Y T, et al. ConsisRec: Enhancing GNN for Social Recommendation via Consistent Neighbor Aggregation // Proc of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York,USA: ACM, 2021: 2141-2145.
[22] XÜ H C, HUANG C, XU Y, et al. Global Context Enhanced Social Recommendation with Hierarchical Graph Neural Network // Proc of the IEEE International Conference on Data Mining. Washington, USA: IEEE, 2020, 701-710.
[23] SALAMAT A, LUO X, JAFARI A.HeteroGraphRec: A Heterogeneous Graph-Based Neural Networks for Social Recommendations. Knowledge-Based Systems, 2021, 217. DOI: 10.1016/j.knosys.2021.106817.
[24] CHEN J J, XIN X, LIANG X F, et al. GDSRec: Graph-Based Decentralized Collaborative Filtering for Social Recommendation. IEEE Transactions on Knowledge and Data Engineering, 2023, 35(5): 4813-4824.
[25] SONG Y Z, YE H L, LI M, et al. Deep Multi-graph Neural Networks with Attention Fusion for Recommendation. Expert Systems with Applications, 2022, 191. DOI: 10.1016/j.eswa.2021.1162
[26] YE H L, SONG Y Z, LI M, et al. A New Deep Graph Attention Approach with Influence and Preference Relationship Reconstruction for Rate Prediction Recommendation. Information Processing and Management, 2023, 60(5). DOI: 10.1016/j.ipm.2023.103
[27] LIU Y, CHEN L, HE X N, et al. Modelling High-Order Social Relations for Item Recommendation. IEEE Transactions on Know-ledge and Data Engineering, 2022, 34(9): 4385-4397.
[28] LI H, WU D M, TANG W B, et al. Overlapping Community Re-gularization for Rating Prediction in Social Recommender // Proc of the 9th ACM Conference on Recommender Systems. New York,USA: ACM, 2015: 27-34.
[29] DING J T, YU G H, HE X N, et al. Sampler Design for Bayesian Personalized Ranking by Leveraging View Data. IEEE Transactions on Knowledge and Data Engineering, 2021, 33(2): 667-681.
[30] HE K M, ZHANG X Y, REN S Q, et al. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification // Proc of the IEEE International Conference on Computer Vision. Washington, USA: IEEE, 2015: 1026-1034.
[31] 邵新茹,叶海良,杨冰,等.基于三阶段生成网络的图像修复.模式识别与人工智能, 2022, 35(12): 1047-1063.
(SHAO X R, YE H L, YANG B, et al. Image Inpainting with a Three-Stage Generative Network. Pattern Recognition and Artificial Intelligence, 2022, 35(12): 1047-1063.)
[32] KOREN Y.Factorization Meets the Neighborhood: A Multifaceted Collaborative Filtering Model // Proc of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York,USA: ACM, 2008: 426-434.
[33] JIA Z H, GAO Q, FAN J.Research on Social Recommendation Model Based on Enhanced Neighbor Perception // Proc of the IEEE International Conference on Systems, Man and Cybernetics. Washington, USA: IEEE, 2022: 3415-3420.
[34] MA X T, DONG L Y, WANG Y Q, et al. An Enhanced Attentive Implicit Relation Embedding for Social Recommendation. Data and Knowledge Engineering, 2023, 145. DOI: 10.1016/j.datak.2023.102142.