Training-free Adjustable Polynomial Graph Filtering for Ultra-fast Multimodal Recommendation
This addresses efficiency and accuracy challenges in multimodal recommendation for users and platforms, though it is incremental as it builds on existing graph filtering techniques.
The paper tackles the high computational cost of neural network-based multimodal recommender systems by proposing a training-free graph filtering method, which improves recommendation accuracy by up to 22.25% and reduces runtime to under 10 seconds.
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 MultiModal-Graph Filtering (MM-GF), a training-free method grounded in graph filtering (GF) for efficient and accurate multimodal recommendations. Specifically, MM-GF first constructs multiple similarity graphs for two distinct modalities as well as user-item interaction data. Then, MM-GF optimally fuses these multimodal signals using a polynomial graph filter that allows for precise control of the frequency response by adjusting frequency bounds. Furthermore, the filter coefficients are treated as hyperparameters, enabling flexible and data-driven adaptation. Extensive experiments on real-world benchmark datasets demonstrate that MM-GF not only improves recommendation accuracy by up to 22.25% compared to the best competitor but also dramatically reduces computational costs by achieving the runtime of less than 10 seconds.