CDGAN: Collaborative Diffusion Generative Adversarial Networks for Recommendation Systems

Abstract

Deep generative models are widely used in recommendation systems because of their ability to deal with uncertainty by learning inherent data distribution. Among deep generative models, Generative Adversarial Networks (GAN) perform well in recommendation tasks. However, existing Collaborative Filtering (CF) recommendation algorithms based on GAN generally have problems such as mode collapse and training instability, which are further aggravated by sparse and noisy recommendation data. To solve these problems, a Collaborative Diffusion Generative Adversarial Networks (CDGAN) framework for recommendation systems is proposed in this paper. Specifically, CDGAN framework is mainly composed of three parts: feature encoder, diffusion generator, and self-attention discriminator. The feature encoder extracts the rating information and side information to obtain potential feature vectors to alleviate the data sparsity problem. The diffusion generator simulates the complex nonlinear mode of the user-item interaction matrix through forward diffusion and reverse denoising to reconstruct the user-item interaction matrix to alleviate the problem of data noise. The self-attention discriminator obtains the user's specific behaviors and preferences through the self-attention mechanism to improve the discriminator's discriminating ability. Furthermore, a corresponding CDGAN recommendation algorithm is designed based on our proposed CDGAN framework. Comprehensive experiments are conducted on three real-world recommendation datasets. The experimental results indicate that, when compared with multiple representative recommendation models, the proposed CDGAN model achieves superior performance in the evaluation metrics Precision and Recall on all datasets, thereby proving its effectiveness.