Mirror Gradient: Towards Robust Multimodal Recommender Systems via Exploring Flat Local Minima

Abstract: Multimodal recommender systems utilize various types of information to model user preferences and item features, helping users discover items aligned with their interests. The integration of multimodal information mitigates the inherent challenges in recommender systems, e.g., the data sparsity problem and cold-start issues. However, it simultaneously magnifies certain risks from multimodal information inputs, such as information adjustment risk and inherent noise risk. These risks pose crucial challenges to the robustness of recommendation models. In this paper, we analyze multimodal recommender systems from the novel perspective of flat local minima and propose a concise yet effective gradient strategy called Mirror Gradient (MG). This strategy can implicitly enhance the model's robustness during the optimization process, mitigating instability risks arising from multimodal information inputs. We also provide strong theoretical evidence and conduct extensive empirical experiments to show the superiority of MG across various multimodal recommendation models and benchmarks. Furthermore, we find that the proposed MG can complement existing robust training methods and be easily extended to diverse advanced recommendation models, making it a promising new and fundamental paradigm for training multimodal recommender systems. The code is released at https://github.com/Qrange-group/Mirror-Gradient.

• The paper's main contribution is the introduction of Mirror Gradient (MG) which steers the training process towards flat local minima to enhance robustness.
• The methodology adjusts gradient directions to balance between escaping sharp minima and settling into flatter regions, reducing sensitivity to noise.
• Experimental results demonstrate improved top-5 recommendation performance and resilience against information noise across different models.

Mirror Gradient: Towards Robust Multimodal Recommender Systems via Exploring Flat Local Minima

Introduction

The paper "Mirror Gradient: Towards Robust Multimodal Recommender Systems via Exploring Flat Local Minima" explores enhancing the robustness of multimodal recommender systems by leveraging the concept of flat local minima. Multimodal recommenders integrate various information forms, such as text and images, to model user preferences and item features, addressing issues like data sparsity and cold-start. However, these systems face challenges from information adjustment and inherent noise risks. This paper proposes a new gradient strategy, Mirror Gradient (MG), to enhance model robustness against these risks.

Multimodal Recommender Systems

Multimodal recommender systems aim to integrate data from different modalities (e.g., text, images) to improve recommendation quality. They are advantageous in addressing the data sparsity problem inherent in traditional recommendation systems, which typically rely on sparse user-item interaction data. Despite these benefits, multimodal systems must handle the complexities introduced by integrating disparate data types, such as dealing with potential noise and frequent changes in item presentation (e.g., visual and textual adjustments).

Figure 1: An illustrative example of multimodal risks.

Flat Local Minima and Robustness

The paper investigates robustness through the lens of flat local minima. Flat local minima refer to regions in the loss landscape where small changes in the input result in minimal changes in the loss, indicating robustness to input perturbations. In contrast, sharp minima are vulnerable to such perturbations, potentially degrading model performance during inference when the input data slightly deviates from expected distributions.

Figure 2: Illustration of flat local minima showing robust vs. vulnerable parameter settings.

Mirror Gradient (MG) Strategy

MG is designed to guide the learning process toward flatter minima, enhancing robustness. It strategically adjusts gradient directions during training to balance between aggressive parameter updates required to escape sharp minima and the controlled steps needed to settle into flat minima. This approach maintains computational efficiency while improving model robustness across various scenarios. Unlike prior adversarial methods that explicitly model data perturbations, MG offers a theoretically grounded, implicit regularization technique.

Theoretical Foundations

The paper provides rigorous theoretical insights supporting MG's effectiveness. Through the lens of loss landscape shaping, it illustrates how MG modifies the effective loss function to penalize sharp minima indirectly. The strategy effectively leads to optimizing an objective that integrates terms favoring flat loss regions, thus promoting robustness naturally.

Experimental Results

Performance Evaluation

Extensive experiments conducted on datasets like Baby, Sports, and Clothing reveal MG's efficacy. The strategy consistently improves top-5 recommendation performance across various models, including VBPR, GRCN, and DualGNN.

Figure 3: Visualization of local minima illustrating the improved stability with MG.

Mitigating Information Noise and Adjustment

MG demonstrates significant resilience to input noise and dynamic information adjustments typically encountered in real-world scenarios. Controlled experiments introduce Gaussian noise into embeddings and simulate textual alterations, consistently showing reduced performance degradation in MG-trained models.

Figure 4: Convergence of MG on the Baby dataset, highlighting improved loss stabilization.

Compatibility and Versatility

MG is compatible with various optimization algorithms (e.g., SGD, Adam), making it versatile for integration into existing training workflows. It complements other robust training techniques, such as adversarial training, without additional computational overhead beyond regular SGD updates.

Conclusion

Mirror Gradient introduces a practical approach for enhancing the robustness of multimodal recommender systems. By encouraging flat local minima, the methodology offers a straightforward yet powerful means to mitigate common robustness issues like information noise and adjustment risks. Future explorations could explore adaptive mechanisms for MG's hyperparameters or extending its utility to other AI domains where robustness is paramount.