Query2Prod2Vec Grounded Word Embeddings for eCommerce
arXiv:2104.02061
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.
arXiv commentPublished as a conference paper at NAACL2021 - Industry Track
References in corpus (8)
- Efficient Estimation of Word Representations in Vector Space
- Distributed Representations of Words and Phrases and their Compositionality
- What the [MASK]? Making Sense of Language-Specific BERT Models
- Overview of the TREC 2021 deep learning track
- Fantastic Embeddings and How to Align Them: Zero-Shot Inference in a Multi-Shop Scenario
- Revisiting Skip-Gram Negative Sampling Model with Rectification
- Blending Search and Discovery: Tag-Based Query Refinement with Contextual Reinforcement Learning
- BERT Goes Shopping: Comparing Distributional Models for Product Representations