Effective recommendation systems rely on capturing user preferences, often
requiring incorporating numerous features such as universally unique
identifiers (UUIDs) of entities. However, the exceptionally high cardinality of
UUIDs poses a significant challenge in terms of model degradation and increased
model size due to sparsity. This paper presents two innovative techniques to
address the challenge of high cardinality in recommendation systems.
Specifically, we propose a bag-of-words approach, combined with layer sharing,
to substantially decrease the model size while improving performance. Our
techniques were evaluated through offline and online experiments on Uber use
cases, resulting in promising results demonstrating our approach's
effectiveness in optimizing recommendation systems and enhancing their overall
performance.

该论文提出了两种创新技术来解决推荐系统中高基数的挑战，通过采用词袋模型和层共享相结合的方法，显著减少模型大小并提高性能，在优步的使用案例中进行了离线和在线实验，取得了有希望的结果，展示了优化推荐系统及提高其整体性能的有效方法。