  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.
