# Query2Prod2Vec Grounded Word Embeddings for eCommerce

arXiv: https://arxiv.org/abs/2104.02061 · HTML: https://mlsanity.com/paper/2104.02061/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 43 references that are in the map.
- First posted on arXiv 2 Apr 2021.

## Builds on (most cited first, 21 of 21)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Energy and Policy Considerations for Deep Learning in NLP](https://mlsanity.com/paper/1906.02243/) (2019)
- [A Primer in BERTology: What we know about how BERT works](https://mlsanity.com/paper/2002.12327/) (2020)
- [Document Ranking with a Pretrained Sequence-to-Sequence Model](https://mlsanity.com/paper/2003.06713/) (2020)
- [Overview of the TREC 2019 deep learning track](https://mlsanity.com/paper/2003.07820/) (2020)
- [Experience Grounds Language](https://mlsanity.com/paper/2004.10151/) (2020)
- [What the [MASK]? Making Sense of Language-Specific BERT Models](https://mlsanity.com/paper/2003.02912/) (2020)
- [Meta-Prod2Vec - Product Embeddings Using Side-Information for Recommendation](https://mlsanity.com/paper/1607.07326/) (2016)
- [E-commerce in Your Inbox: Product Recommendations at Scale](https://mlsanity.com/paper/1606.07154/) (2016)
- [Fantastic Embeddings and How to Align Them: Zero-Shot Inference in a Multi-Shop Scenario](https://mlsanity.com/paper/2007.14906/) (2020)
- [Scalable Semantic Matching of Queries to Ads in Sponsored Search Advertising](https://mlsanity.com/paper/1607.01869/) (2016)
- [BERT Goes Shopping: Comparing Distributional Models for Product Representations](https://mlsanity.com/paper/2012.09807/) (2020)
- [How to Grow a (Product) Tree: Personalized Category Suggestions for eCommerce Type-Ahead](https://mlsanity.com/paper/2005.12781/) (2020)
- [Shopping in the Multiverse: A Counterfactual Approach to In-Session Attribution](https://mlsanity.com/paper/2007.10087/) (2020)
- ["An Image is Worth a Thousand Features": Scalable Product Representations for In-Session Type-Ahead Personalization](https://mlsanity.com/paper/2003.07160/) (2020)
- [Blending Search and Discovery: Tag-Based Query Refinement with Contextual Reinforcement Learning](https://mlsanity.com/paper/2010.09495/) (2020)

## Built on by (most cited first, 8 of 8)

- [Multimodal Garment Designer: Human-Centric Latent Diffusion Models for Fashion Image Editing](https://mlsanity.com/paper/2304.02051/) (2023)
- [Contrastive language and vision learning of general fashion concepts](https://mlsanity.com/paper/2204.03972/) (2022)
- [Language in a (Search) Box: Grounding Language Learning in Real-World Human-Machine Interaction](https://mlsanity.com/paper/2104.08874/) (2021)
- [Reasonable Scale Machine Learning with Open-Source Metaflow](https://mlsanity.com/paper/2303.11761/) (2023)
- [Multimodal-Conditioned Latent Diffusion Models for Fashion Image Editing](https://mlsanity.com/paper/2403.14828/) (2024)
- ["Are you sure?": Preliminary Insights from Scaling Product Comparisons to Multiple Shops](https://mlsanity.com/paper/2107.03256/) (2021)
- ["Does it come in black?" CLIP-like models are zero-shot recommenders](https://mlsanity.com/paper/2204.02473/) (2022)
- [ITEm: Unsupervised Image-Text Embedding Learning for eCommerce](https://mlsanity.com/paper/2311.02084/) (2023)

## Abstract (arXiv)

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.