Query2Prod2Vec Grounded Word Embeddings for eCommerce
This work addresses data efficiency and practical constraints for product search, particularly benefiting eCommerce practitioners outside of retail giants, though it appears incremental.
The paper tackles the problem of improving product search by grounding word embeddings in product embeddings, using shopping sessions and merchandising annotations, and reports that their model is more accurate than existing NLP and IR techniques.
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.