A Fashion Item Recommendation Model in Hyperbolic Space

Ryotaro Shimizu, Yu Wang, Masanari Kimura, Yuki Hirakawa, Takashi Wada, Yuki Saito, Julian McAuley; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 8377-8383

In this work we propose a fashion item recommendation model that incorporates hyperbolic geometry into user and item representations. Using hyperbolic space our model aims to capture implicit hierarchies among items based on their visual data and users' purchase history. During training we apply a multi-task learning framework that considers both hyperbolic and Euclidean distances in the loss function. Our experiments on three data sets show that our model performs better than previous models trained in Euclidean space only confirming the effectiveness of our model. Our ablation studies show that multi-task learning plays a key role and removing the Euclidean loss substantially deteriorates the model performance.