Attention-based Fusion for Outfit Recommendation
This paper describes an attention-based fusion method for outfit recommendation which fuses the information in the product image and description to capture the most important, fine-grained product features into the item representation. We experiment with different kinds of attention mechanisms and demonstrate that the attention-based fusion improves item understanding. We outperform state-of-the-art outfit recommendation results on three benchmark datasets.
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