Training-free Adjustable Polynomial Graph Filtering for Ultra-fast Multimodal Recommendation

ArXi:2503.04406v3 Announce Type: replace-cross Multimodal recommender systems improve the performance of canonical recommender systems with no item features by utilizing diverse content types such as text, images, and videos, while alleviating inherent sparsity of user-item interactions and accelerating user engagement. However, current neural network-based models often incur significant computational overhead due to the complex