Ecommerce

Query2Prod2Vec: Grounded Word Embeddings for eCommerce

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.

Fantastic Embeddings and How to Align Them: Zero-Shot Inference in a Multi-Shop Scenario

In this paper we work on aligning product embeddings that come from different shops. We use techniques from machine translation to provide an effective method for alignment.