Query2Prod2Vec Grounded Word Embeddings for eCommerce

• URL: http://arxiv.org/abs/2104.02061v1
• Date: Fri, 2 Apr 2021 21:32:43 GMT
• Title: Query2Prod2Vec Grounded Word Embeddings for eCommerce
• Authors: Federico Bianchi, Jacopo Tagliabue and Bingqing Yu
• Abstract summary: We present a model that grounds lexical representations for product search in product embeddings. We leverage shopping sessions to learn the underlying space and use merchandising annotations to build lexical analogies for evaluation.
• Score: 4.137464623395377
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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