Query2Prod2Vec: Grounded Word Embeddings for eCommerce

Abstract

We present Query2Prod2Vec, a model that grounds lexical representations for product search in product embeddings: in our model, meaning is a mapping between words and a latent space of products in a digital shop. We leverage shopping sessions to learn the underlying space and use merchandising annotations to build lexical analogies for evaluation: our experiments show that our model is more accurate than known techniques from the NLP and IR literature. Finally, we stress the importance of data efficiency for product search outside of retail giants, and highlight how Query2Prod2Vec fits with practical constraints faced by most practitioners.

Publication
In Proceedings of The North American Chapter of the Association for Computational Linguistics
Federico Bianchi
Federico Bianchi
Postdoctoral Researcher

My research interests include meaning in natural language and programming languages.

Related