Query2Prod2Vec Grounded Word Embeddings for eCommerce

2021/04/02 by Federico Bianchi, Jacopo Tagliabue, Bianchi, Federico +3
Computer Science · #Advanced Text Analysis Techniques #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Machine Learning (cs.LG) #Sentiment Analysis and Opinion Mining #Topic Modeling

paper · pdf · doi:10.48550/arxiv.2104.02061

openalex publication_date 2021/04/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• Efficient Estimation of Word Representations in Vector Space
• What the [MASK]? Making Sense of Language-Specific BERT Models

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