  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.
