Adaptive Adjustment Graph Augmentation and Representation Structures for Recommendation Model

doi:10.19678/j.issn.1000-3428.0070167

Abstract

Abstract:

Collaborative Filtering (CF) is an effective recommendation method that predicts user preferences by learning the representations of users and items. A recent study on CF has improved representation quality and enhanced recommendation performance from the perspective of hypersphere alignment and uniformity. The present study promotes alignment to increase the similarity between the representations of interacting users and items and enhances uniformity, resulting in a more evenly distributed representation of users and items within the hyper sphere. However, the use of only supervised data for alignment and uniform representation optimization ignores issues such as behavioral noise, data sparsity, and differences in popularity, which inevitably damage the generalization performance and structural characteristics of the representation. To address these issues, a more accurate adaptive alignment and uniform recommendation model is proposed. The data is modeled as a bipartite graph of user-item interaction and a Graph Neural Network (GNN) is applied to learn user and item representations. The model performs self-supervised contrastive learning on user and project representations to capture additional graph structure patterns unrelated to the supervised data. During optimization, the alignment and uniformity optimization objectives are adaptively adjusted based on popularity, thereby achieving a more generalized alignment and uniformity. Extensive experiments are conducted on three real-world datasets, and the results demonstrate the superiority and robustness of the proposed model over the baseline models.

Key words: recommendation system, Graph Neural Network (GNN), contrastive learning, data augmentation, alignment and uniformity

摘要：

协同过滤(CF)被认为是一种有效的推荐方法, 它可以通过学习用户和项目的表示来预测用户偏好。最近关于CF的一项研究从超球体对齐和均匀性的角度来提高表示质量, 增强了推荐性能。该研究促进对齐以增加交互用户和项目的表示之间的相似性, 并增强均匀性, 使超球体内拥有更均匀分布的用户和项目表示。然而, 仅使用监督数据进行对齐与均匀的表示优化会忽略行为噪声、数据稀疏和流行度差异等问题, 这难免会损害表示的泛化性能和结构特性。为了解决这些问题, 提出一种更准确的适应性对齐与均匀的推荐模型。将数据建模为用户-项目交互的二分图, 并应用图神经网络(GNN)来学习用户和项目表示。模型对用户和项目表示进行自监督对比学习, 从而捕获更多与监督数据无关的图结构模式。同时, 在优化时根据流行度来适应性地调整对齐和均匀的优化目标, 从而实现更广义的对齐和均匀。在3个真实世界数据集上进行大量实验, 结果证明了所提模型相对基线模型的优越性和稳健性。

关键词: 推荐系统, 图神经网络, 对比学习, 数据增强, 对齐和均匀

GUO Tiansheng, XIE Jinkui. Adaptive Adjustment Graph Augmentation and Representation Structures for Recommendation Model[J]. Computer Engineering, 2026, 52(2): 69-78.

郭天晟, 谢瑾奎. 自适应调节图增强与表示结构的推荐模型[J]. 计算机工程, 2026, 52(2): 69-78.

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Figures/Tables 10

Fig.1 Overall framework of the AdapGAU model

Fig.2 Adaptive alignment and uniformity of embeddings

Fig.3 Model performance under different noises

Fig.4 Alignment of user-item pairs with different popularity level

Fig.5 Embedding visualization

Fig.6 Influence of λ

Fig.7 Influence of τ

References 32

1	何杏宇, 周易歆, 罗东旭, 等. 基于图神经网络和多主体评价的教学资源推荐. 计算机工程, 2024, 50 (7): 13- 22. doi: 10.19678/j.issn.1000-3428.0069584
	HE X Y , ZHOU Y X , LUO D X , et al. Education resource recommendation based on graph neural network and multi-subject rating. Computer Engineering, 2024, 50 (7): 13- 22. doi: 10.19678/j.issn.1000-3428.0069584
2	BEN SCHAFER J, FRANKOWSKI D, HERLOCKER J, et al. Collaborative filtering recommender systems[EB/OL]. [2024-05-05]. https://link.springer.com/chapter/10.1007/978-3-540-72079-9_9.
3	姚迅, 王海鹏, 胡新荣, 等. 基于自适应增强的多视图对比推荐算法. 计算机工程, 2025, 51 (5): 103- 113. doi: 10.19678/j.issn.1000-3428.0069219
	YAO X , WANG H P , HU X R , et al. Multi-view contrastive recommendation algorithm based on adaptive enhancement. Computer Engineering, 2025, 51 (5): 103- 113. doi: 10.19678/j.issn.1000-3428.0069219
4	WANG T, ISOLA P. Understanding contrastive representation learning through alignment and uniformity on the hypersphere[EB/OL]. [2024-05-05]. https://arxiv.org/abs/2005.10242.
5	WANG C Y, YU Y Q, MA W Z, et al. Towards representation alignment and uniformity in collaborative filtering[C]//Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, USA: ACM Press, 2022: 1816-1825.
6	YANG L W, LIU Z W, WANG C, et al. Graph-based alignment and uniformity for recommendation[C]//Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. New York, USA: ACM Press, 2023: 4395-4399.
7	OUYANG Z Y , ZHANG C H , HOU S F , et al. How to improve representation alignment and uniformity in graph-based collaborative filtering?. Proceedings of the International AAAI Conference on Web and Social Media, 2024, 18, 1148- 1159. doi: 10.1609/icwsm.v18i1.31379
8	YING R, HE R N, CHEN K F, et al. Graph convolutional neural networks for web-scale recommender systems[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA: ACM Press, 2018: 974-983.
9	WANG X, HE X N, WANG M, et al. Neural graph collaborative filtering[C]//Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, USA: ACM Press, 2019: 165-174.
10	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, USA: ACM Press, 2020: 639-648.
11	MAO K L, ZHU J M, XIAO X, et al. UltraGCN: ultra simplification of graph convolutional networks for recommendation[C]//Proceedings of the 30th ACM International Conference on Information and Knowledge Management. New York, USA: ACM Press, 2021: 1253-1262.
12	REN Z C, LIANG S S, LI P J, et al. Social collaborative viewpoint regression with explainable recommendations[C]//Proceedings of the 10th ACM International Conference on Web Search and Data Mining. New York, USA: ACM Press, 2017: 485-494.
13	WANG X, HE X N, CAO Y X, et al. KGAT: knowledge graph attention network for recommendation[C]//Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA: ACM Press, 2019: 950-958.
14	颜祯, 谢瑾奎, 曹磊亮. 基于邻域交互和图神经网络的推荐模型. 小型微型计算机系统, 2023, 44 (7): 1391- 1397.
	YAN Z , XIE J K , CAO L L . Neighborhood interaction and graph neural network for recommendation model. Journal of Chinese Computer Systems, 2023, 44 (7): 1391- 1397.
15	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, USA: ACM Press, 2021: 726-735.
16	KHOSLA P , TETERWAK P , WANG C , et al. Supervised contrastive learning. Advances in Neural Information Processing Systems, 2020, 33, 18661- 18673.
17	GAO T, YAO X, CHEN D. SimCSE: simple contrastive learning of sentence embeddings[EB/OL]. [2024-05-05]. https://arxiv.org/abs/2104.08821.
18	ZHOU K, WANG H, ZHAO W X, et al. S3-Rec: self-supervised learning for sequential recommendation with mutual information maximization[C]//Proceedings of the 29th ACM International Conference on Information and Knowledge Management. New York, USA: ACM Press, 2020: 1893-1902.
19	WEI Y W, WANG X, LI Q, et al. Contrastive learning for cold-start recommendation[C]//Proceedings of the 29th ACM International Conference on Multimedia. New York, USA: ACM Press, 2021: 5382-5390.
20	XIE X, SUN F, LIU Z Y, et al. Contrastive learning for sequential recommendation[C]//Proceedings of the 38th IEEE International Conference on Data Engineering (ICDE). Washington D.C., USA: IEEE Press, 2022: 1259-1273.
21	QIU R H, HUANG Z, YIN H Z, et al. Contrastive learning for representation degeneration problem in sequential recommendation[C]//Proceedings of the 15th ACM International Conference on Web Search and Data Mining. New York, USA: ACM Press, 2022: 813-823.
22	LIN Z H, TIAN C X, HOU Y P, et al. Improving graph collaborative filtering with neighborhood-enriched contrastive learning[C]//Proceedings of the ACM Web Conference 2022. New York, USA: ACM Press, 2022: 2320-2329.
23	CAI X, HUANG C, XIA L, et al. LightGCL: simple yet effective graph contrastive learning for recommendation[EB/OL]. [2024-05-05]. https://arxiv.org/abs/2302.08191.
24	YU J L, YIN H Z, XIA X, et al. Are graph augmentations necessary? simple graph contrastive learning for recommendation[C]//Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, USA: ACM Press, 2022: 1294-1303.
25	RENDLE S, FREUDENTHALER C, GANTNER Z, et al. BPR: Bayesian personalized ranking from implicit feedback[C]//Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence. New York, USA: ACM Press, 2009: 452-461.
26	MAO K L, ZHU J M, WANG J P, et al. SimpleX: a simple and strong baseline for collaborative filtering[C]//Proceedings of the 30th ACM International Conference on Information and Knowledge Management. New York, USA: ACM Press, 2021: 1243-1252.
27	OORD A, LI Y, VINYALS O. Representation learning with contrastive predictive coding[EB/OL]. [2024-05-05]. https://arxiv.org/abs/1807.03748.
28	YU J L , YIN H Z , XIA X , et al. Self-supervised learning for recommender systems: a survey. IEEE Transactions on Knowledge and Data Engineering, 2024, 36 (1): 335- 355. doi: 10.1109/TKDE.2023.3282907
29	KIPF T N, WELLING M. Variational graph auto-encoders[EB/OL]. [2024-05-05]. https://arxiv.org/abs/1611.07308.
30	VAN DER MAATEN L , HINTON G . Visualizing data using t-SNE. Journal of Machine Learning Research, 2008, 9, 2579- 2605.
31	BOTEV Z I , GROTOWSKI J F , KROESE D P . Kernel density estimation via diffusion. The Annals of Statistics, 2010, 38 (5): 16- 23.
32	CHEN J W, WU J K, WU J C, et al. Adap-τ: adaptively modulating embedding magnitude for recommendation[C]//Proceedings of the ACM Web Conference 2023. New York, USA: ACM Press, 2023: 1085-1096.

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