Fantastic Embeddings and How to Align Them: Zero-Shot Inference in a Multi-Shop Scenario

Abstract

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.

accepted at 2020 SIGIR Workshop On eCommerce

References in corpus (3)

• Meta-Graph: Few Shot Link Prediction via Meta Learning
• Large-scale Collaborative Filtering with Product Embeddings
• Personalized Query Auto-Completion Through a Lightweight Representation of the User Context

Cited by in corpus (6)

• Beyond NDCG: behavioral testing of recommender systems with RecList
• You Do Not Need a Bigger Boat: Recommendations at Reasonable Scale in a (Mostly) Serverless and Open Stack
• Shopping in the Multiverse: A Counterfactual Approach to In-Session Attribution
• "Are you sure?": Preliminary Insights from Scaling Product Comparisons to Multiple Shops
• Query2Prod2Vec Grounded Word Embeddings for eCommerce
• Aligning Hotel Embeddings using Domain Adaptation for Next-Item Recommendation