This paper addresses the challenge of leveraging multiple embedding spaces
for multi-shop personalization, proving that zero-shot inference is possible by
transferring shopping intent from one website to another without manual
intervention. We detail a machine learning pipeline to train and optimize
embeddings within shops first, and support the quantitative findings with
additional qualitative insights. We then turn to the harder task of using
learned embeddings across shops: if products from different shops live in the
same vector space, user intent - as represented by regions in this space - can
then be transferred in a zero-shot fashion across websites. We propose and
benchmark unsupervised and supervised methods to "travel" between embedding
spaces, each with its own assumptions on data quantity and quality. We show
that zero-shot personalization is indeed possible at scale by testing the
shared embedding space with two downstream tasks, event prediction and
type-ahead suggestions. Finally, we curate a cross-shop anonymized embeddings
dataset to foster an inclusive discussion of this important business scenario.

本文利用嵌入空间跨越网站实现零 shot 推荐的问题，提出了一种机器学习流程来训练嵌入式内容，并用额外的定性见解支持定量发现，证明多商店个性化推荐是可行的，并且用事件预测和自动完成建议测试了共享嵌入式空间，最后创建了一个跨商店的匿名嵌入式数据集以促进这一重要业务场景的包容性讨论。