Training-Free Graph Filtering via Multimodal Feature Refinement for Extremely Fast Multimodal Recommendation

In this section, in addition to multimodal feature refinement described in the main manuscript, we present three different strategies to construct item-item similarity graphs for textual and visual modalities, as edge weights in each similarity graph are not naturally defined unlike the case of user-item interactions. A. Cosine Similarity Cosine similarity is one of the straightforward approach to calculating similarity between two vectors. We perform kNN sparsification [?] to extract high similarity scores in the similarity matrix: top-k(S B. Pearson Correlation Coefficient Pearson correlation coefficient [?] can be adopted to construct item-item similarity graphs for multiple modalities. C. Gaussian Kernel According to [?], item-item similarity graphs can be constructed using a Gaussian kernel:) ( The best and second-best performers are highlighted in bold and underline, respectively. Figure 1: The effect of β and γ hyperparameters for three benchmark datasets, where the horizontal and vertical axes indicate the value of each hyperparameter and the performance in NDCG@20, respectively.