Query2Prod2Vec: Grounded Word Embeddings for eCommerce

Federico Bianchi, Jacopo Tagliabue, Bingqing Yu


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.
Anthology ID:
2021.naacl-industry.20
Volume:
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Papers
Month:
June
Year:
2021
Address:
Online
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
154–162
Language:
URL:
https://aclanthology.org/2021.naacl-industry.20
DOI:
10.18653/v1/2021.naacl-industry.20
Award:
 Best Industry Paper
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.naacl-industry.20.pdf