# Recommender Systems Based on Generative Adversarial Networks: A Problem-Driven Perspective

arXiv: https://arxiv.org/abs/2003.02474 · HTML: https://mlsanity.com/paper/2003.02474/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Mar 2020.

## Built on by (most cited first, 11 of 11)

- [Double-Scale Self-Supervised Hypergraph Learning for Group Recommendation](https://mlsanity.com/paper/2109.04200/) (2021)
- [Denoising Diffusion Recommender Model](https://mlsanity.com/paper/2401.06982/) (2024)
- [DiffMM: Multi-Modal Diffusion Model for Recommendation](https://mlsanity.com/paper/2406.11781/) (2024)
- [Ready for Emerging Threats to Recommender Systems? A Graph Convolution-based Generative Shilling Attack](https://mlsanity.com/paper/2107.10457/) (2021)
- [Hyperbolic Diffusion Recommender Model](https://mlsanity.com/paper/2504.01541/) (2025)
- [Multi-Resolution Diffusion for Privacy-Sensitive Recommender Systems](https://mlsanity.com/paper/2311.03488/) (2023)
- [Diffusion Models in Recommendation Systems: A Survey](https://mlsanity.com/paper/2501.10548/) (2025)
- [Flexible Generation of Preference Data for Recommendation Analysis](https://mlsanity.com/paper/2407.16594/) (2024)
- [Creating Synthetic Datasets for Collaborative Filtering Recommender Systems using Generative Adversarial Networks](https://mlsanity.com/paper/2303.01297/) (2023)
- [Incorporating Classifier-Free Guidance in Diffusion Model-Based Recommendation](https://mlsanity.com/paper/2409.10494/) (2024)
- [Deep Variational Models for Collaborative Filtering-based Recommender Systems](https://mlsanity.com/paper/2107.12677/) (2021)

## Abstract (arXiv)

Recommender systems (RSs) now play a very important role in the online lives of people as they serve as personalized filters for users to find relevant items from an array of options. Owing to their effectiveness, RSs have been widely employed in consumer-oriented e-commerce platforms. However, despite their empirical successes, these systems still suffer from two limitations: data noise and data sparsity. In recent years, generative adversarial networks (GANs) have garnered increased interest in many fields, owing to their strong capacity to learn complex real data distributions; their abilities to enhance RSs by tackling the challenges these systems exhibit have also been demonstrated in numerous studies. In general, two lines of research have been conducted, and their common ideas can be summarized as follows: (1) for the data noise issue, adversarial perturbations and adversarial sampling-based training often serve as a solution; (2) for the data sparsity issue, data augmentation--implemented by capturing the distribution of real data under the minimax framework--is the primary coping strategy. To gain a comprehensive understanding of these research efforts, we review the corresponding studies and models, organizing them from a problem-driven perspective. More specifically, we propose a taxonomy of these models, along with their detailed descriptions and advantages. Finally, we elaborate on several open issues and current trends in GAN-based RSs.