@inproceedings{bianchi-etal-2021-query2prod2vec,
title = "{Q}uery2{P}rod2{V}ec: Grounded Word Embeddings for e{C}ommerce",
author = "Bianchi, Federico and
Tagliabue, Jacopo and
Yu, Bingqing",
editor = "Kim, Young-bum and
Li, Yunyao and
Rambow, Owen",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Papers",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-industry.20",
doi = "10.18653/v1/2021.naacl-industry.20",
pages = "154--162",
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.",
}
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%0 Conference Proceedings
%T Query2Prod2Vec: Grounded Word Embeddings for eCommerce
%A Bianchi, Federico
%A Tagliabue, Jacopo
%A Yu, Bingqing
%Y Kim, Young-bum
%Y Li, Yunyao
%Y Rambow, Owen
%S Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Papers
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F bianchi-etal-2021-query2prod2vec
%X 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.
%R 10.18653/v1/2021.naacl-industry.20
%U https://aclanthology.org/2021.naacl-industry.20
%U https://doi.org/10.18653/v1/2021.naacl-industry.20
%P 154-162
Markdown (Informal)
[Query2Prod2Vec: Grounded Word Embeddings for eCommerce](https://aclanthology.org/2021.naacl-industry.20) (Bianchi et al., NAACL 2021)
ACL
- Federico Bianchi, Jacopo Tagliabue, and Bingqing Yu. 2021. Query2Prod2Vec: Grounded Word Embeddings for eCommerce. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Papers, pages 154–162, Online. Association for Computational Linguistics.