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

CLC Number:

TP391

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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