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

arXiv:2402.11262v1 Announce Type: cross
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 …

arxiv cs.ir cs.lg gradient multimodal recommender systems robust systems type via