OutfitTransformer: Outfit Representations for Fashion Recommendation
Rohan Sarkar, Navaneeth Bodla, Mariya I. Vasileva, Yen-Liang Lin, Anurag Beniwal, Alan Lu, Gérard Medioni
Abstract
Predicting outfit compatibility and retrieving complementary items are critical components for a fashion recommendation system. We present a scalable framework, OutfitTransformer, that learns compatibility of the entire out- fit and supports large-scale complementary item retrieval. We model outfits as an unordered set of items and leverage self-attention mechanism to learn the relationships between items. We train the framework using a proposed set-wise outfit ranking loss to generate a target item embedding given an outfit, and a target item specification. The generated target item embedding is then used to retrieve compatible items that match the outfit. Experimental results demonstrate that our approach outperforms state-of-the-art methods on compatibility prediction, fill-in-the-blank, and complementary item retrieval tasks.