  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.
