Attention-based Fusion for Outfit Recommendation
This work addresses the problem of improving outfit recommendations for users by enhancing item representations, though it appears incremental as it builds on existing attention methods.
The paper tackled outfit recommendation by fusing product image and description information using attention mechanisms to capture fine-grained features, resulting in state-of-the-art performance on three benchmark datasets.
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.