Recommendation systems have become indispensable in various online platforms, from e-commerce to streaming services. A fundamental challenge in this domain is learning effective embeddings from sparse user-item interactions. While contrastive learning has recently emerged as a promising solution to this issue, generating augmented views for contrastive learning through most existing random data augmentation methods often leads to the alteration of original semantic information. In this paper, we propose a novel framework, GDA4Rec (Generative Data Augmentation in graph contrastive learning for Recommendation) to generate high-quality augmented views and provide robust self-supervised signals. Specifically, we employ a noise generation module that leverages deep generative models to approximate the distribution of original data for data augmentation. Additionally, GDA4Rec further extracts an item complement matrix to characterize the latent correlations between items and provide additional self-supervised signals. Lastly, a joint objective that integrates recommendation, data augmentation and contrastive learning is used to enforce the model to learn more effective and informative embeddings. Extensive experiments are conducted on three public datasets to demonstrate the superiority of the model. The code is available at: https://github.com/MrYansong/GDA4Rec.

翻译：推荐系统已成为从电子商务到流媒体服务等各种在线平台不可或缺的组成部分。该领域的一个基本挑战是从稀疏的用户-物品交互中学习有效的嵌入表示。尽管对比学习最近已成为解决此问题的一种有前景的方案，但通过大多数现有的随机数据增强方法为对比学习生成增强视图，常常会导致原始语义信息的改变。本文提出了一种新颖的框架 GDA4Rec（推荐系统中图对比学习的生成式数据增强），用于生成高质量的增强视图并提供鲁棒的自监督信号。具体而言，我们采用了一个噪声生成模块，该模块利用深度生成模型来逼近原始数据的分布以进行数据增强。此外，GDA4Rec 进一步提取了一个物品互补矩阵，以刻画物品之间的潜在关联并提供额外的自监督信号。最后，通过一个整合了推荐、数据增强和对比学习的联合目标函数，迫使模型学习更有效且信息更丰富的嵌入表示。我们在三个公共数据集上进行了广泛的实验，以证明该模型的优越性。代码可在以下网址获取：https://github.com/MrYansong/GDA4Rec。