用双曲空间建模时尚商品层级，提升推荐精度。
A Fashion Item Recommendation Model in Hyperbolic Space
- 将用户与商品嵌入双曲空间，捕捉视觉与购买历史中的隐式层次结构。
- 在三个数据集上优于纯欧氏空间模型，多任务学习显著提升性能。
- 适合研究推荐系统中层次结构建模的学者或工业界工程师。
本文提出一种将双曲几何融入用户与商品表示的时尚商品推荐模型。通过双曲空间，模型旨在基于商品视觉数据和用户购买历史捕捉商品间的隐式层级关系。训练时采用多任务学习框架，在损失函数中同时考虑双曲距离与欧氏距离。在三个数据集上的实验表明，该模型性能优于仅在欧氏空间训练的模型，验证了其有效性。消融实验显示，多任务学习起关键作用，移除欧氏损失会显著降低模型表现。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。