[2503.04406] Training-free Adjustable Polynomial Graph Filtering for Ultra-fast Multimodal Recommendation

Computer Science > Information Retrieval arXiv:2503.04406 (cs) [Submitted on 6 Mar 2025 (v1), last revised 24 Mar 2026 (this version, v3)] Title:Training-free Adjustable Polynomial Graph Filtering for Ultra-fast Multimodal Recommendation Authors:Yu-Seung Roh, Joo-Young Kim, Jin-Duk Park, Won-Yong Shin View a PDF of the paper titled Training-free Adjustable Polynomial Graph Filtering for Ultra-fast Multimodal Recommendation, by Yu-Seung Roh and 3 other authors View PDF HTML (experimental) Abstract: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 training process required to learn and integrate information from multiple modalities. To address this challenge, we propose a training-free multimodal recommendation method grounded in graph filtering, designed for multimodal recommendation systems to achieve efficient and accurate recommendation. Specifically, the proposed method first constructs multiple similarity graphs for two distinct modalities as well as user-item interaction data. Then, it optimally fuses these multimodal signals using a polynomial graph filter that allows for precise control of the frequency response by adjusting frequency b...