# Adversarial Training Towards Robust Multimedia Recommender System

arXiv: https://arxiv.org/abs/1809.07062 · HTML: https://mlsanity.com/paper/1809.07062/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Sep 2018.

## Built on by (most cited first, 28 of 28)

- [Recommender Systems in the Era of Large Language Models (LLMs)](https://mlsanity.com/paper/2307.02046/) (2023)
- [A Comprehensive Survey on Trustworthy Recommender Systems](https://mlsanity.com/paper/2209.10117/) (2022)
- [Enhancing Knowledge Tracing via Adversarial Training](https://mlsanity.com/paper/2108.04430/) (2021)
- [Towards Unified Multi-Modal Personalization: Large Vision-Language Models for Generative Recommendation and Beyond](https://mlsanity.com/paper/2403.10667/) (2024)
- [Recent Developments in Recommender Systems: A Survey](https://mlsanity.com/paper/2306.12680/) (2023)
- [Defending Substitution-Based Profile Pollution Attacks on Sequential Recommenders](https://mlsanity.com/paper/2207.11237/) (2022)
- [PORE: Provably Robust Recommender Systems against Data Poisoning Attacks](https://mlsanity.com/paper/2303.14601/) (2023)
- [From Intrinsic to Counterfactual: On the Explainability of Contextualized Recommender Systems](https://mlsanity.com/paper/2110.14844/) (2021)
- [Mirror Gradient: Towards Robust Multimodal Recommender Systems via Exploring Flat Local Minima](https://mlsanity.com/paper/2402.11262/) (2024)
- [Adversarial Item Promotion on Visually-Aware Recommender Systems by Guided Diffusion](https://mlsanity.com/paper/2312.15826/) (2023)
- [ToDA: Target-oriented Diffusion Attacker against Recommendation System](https://mlsanity.com/paper/2401.12578/) (2024)
- [Towards Adversarially Robust Recommendation from Adaptive Fraudster Detection](https://mlsanity.com/paper/2211.11534/) (2022)
- [FINEST: Stabilizing Recommendations by Rank-Preserving Fine-Tuning](https://mlsanity.com/paper/2402.03481/) (2024)
- [Efficient and Responsible Adaptation of Large Language Models for Robust and Equitable Top-k Recommendations](https://mlsanity.com/paper/2501.04762/) (2025)
- [Robustness-via-Synthesis: Robust Training with Generative Adversarial Perturbations](https://mlsanity.com/paper/2108.09713/) (2021)
- [Towards Robust Recommender Systems via Triple Cooperative Defense](https://mlsanity.com/paper/2210.13762/) (2022)
- [Securing Visually-Aware Recommender Systems: An Adversarial Image Reconstruction and Detection Framework](https://mlsanity.com/paper/2306.07992/) (2023)
- [Adversarial Text Rewriting for Text-aware Recommender Systems](https://mlsanity.com/paper/2408.00312/) (2024)
- [Recommender Systems for Democracy: Toward Adversarial Robustness in Voting Advice Applications](https://mlsanity.com/paper/2505.13329/) (2025)
- [IndirectAD: Practical Data Poisoning Attacks against Recommender Systems for Item Promotion](https://mlsanity.com/paper/2511.05845/) (2025)
- [Understanding the Effects of Adversarial Personalized Ranking Optimization Method on Recommendation Quality](https://mlsanity.com/paper/2107.13876/) (2021)
- [Towards User-Centered Metrics for Trustworthy AI in Immersive Cyberspace](https://mlsanity.com/paper/2203.03718/) (2022)
- [Discussion about Attacks and Defenses for Fair and Robust Recommendation System Design](https://mlsanity.com/paper/2210.07817/) (2022)
- [Securing Recommender System via Cooperative Training](https://mlsanity.com/paper/2401.12700/) (2024)
- [VisualLens: Personalization through Task-Agnostic Visual History](https://mlsanity.com/paper/2411.16034/) (2024)
- [Towards Explainable Personalized Recommendations by Learning from Users' Photos](https://mlsanity.com/paper/2510.21455/) (2025)
- [MAB-DQA: Addressing Query Aspect Importance in Document Question Answering with Multi-Armed Bandits](https://mlsanity.com/paper/2604.08952/) (2026)
- [Band Together: Untargeted Adversarial Training with Multimodal Coordination against Evasion-based Promotion Attacks](https://mlsanity.com/paper/2605.06238/) (2026)

## Abstract (arXiv)

With the prevalence of multimedia content on the Web, developing recommender solutions that can effectively leverage the rich signal in multimedia data is in urgent need. Owing to the success of deep neural networks in representation learning, recent advance on multimedia recommendation has largely focused on exploring deep learning methods to improve the recommendation accuracy. To date, however, there has been little effort to investigate the robustness of multimedia representation and its impact on the performance of multimedia recommendation. In this paper, we shed light on the robustness of multimedia recommender system. Using the state-of-the-art recommendation framework and deep image features, we demonstrate that the overall system is not robust, such that a small (but purposeful) perturbation on the input image will severely decrease the recommendation accuracy. This implies the possible weakness of multimedia recommender system in predicting user preference, and more importantly, the potential of improvement by enhancing its robustness. To this end, we propose a novel solution named Adversarial Multimedia Recommendation (AMR), which can lead to a more robust multimedia recommender model by using adversarial learning. The idea is to train the model to defend an adversary, which adds perturbations to the target image with the purpose of decreasing the model's accuracy. We conduct experiments on two representative multimedia recommendation tasks, namely, image recommendation and visually-aware product recommendation. Extensive results verify the positive effect of adversarial learning and demonstrate the effectiveness of our AMR method. Source codes are available in https://github.com/duxy-me/AMR.