mlsanityData to 13 Oct 2026

Query2Prod2Vec Grounded Word Embeddings for eCommerce

arXiv 2104.02061 ↗First posted 2 Apr 2021cs.IR cs.LGcited by 8

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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.

Key facts. Cited by 8 papers in the map (data through 13 Oct 2026). Builds on 21 of its 43 references that are in the map. First posted on arXiv 2 Apr 2021.

Builds on

21 of 43 references.

References · 21

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9 more
  1. 2016Meta-Prod2Vec - Product Embeddings Using Side-Information for Recommendationcited by 30
  2. 2016E-commerce in Your Inbox: Product Recommendations at Scalecited by 28
  3. 2020Fantastic Embeddings and How to Align Them: Zero-Shot Inference in a Multi-Shop Scenariocited by 16
  4. 2016Scalable Semantic Matching of Queries to Ads in Sponsored Search Advertisingcited by 12
  5. 2020BERT Goes Shopping: Comparing Distributional Models for Product Representationscited by 7
  6. 2020How to Grow a (Product) Tree: Personalized Category Suggestions for eCommerce Type-Aheadcited by 6
  7. 2020Shopping in the Multiverse: A Counterfactual Approach to In-Session Attributioncited by 5
  8. 2020"An Image is Worth a Thousand Features": Scalable Product Representations for In-Session Type-Ahead Personalizationcited by 4
  9. 2020Blending Search and Discovery: Tag-Based Query Refinement with Contextual Reinforcement Learningcited by 1

Built on by

Cited by year

Citing papers per year: 2021: 2; 2022: 2; 2023: 3; 2024: 1.

8 papers.

  1. 2023Multimodal Garment Designer: Human-Centric Latent Diffusion Models for Fashion Image Editingcited by 62
  2. 2022Contrastive language and vision learning of general fashion conceptscited by 41
  3. 2021Language in a (Search) Box: Grounding Language Learning in Real-World Human-Machine Interactioncited by 5
  4. 2023Reasonable Scale Machine Learning with Open-Source Metaflowcited by 5
  5. 2024Multimodal-Conditioned Latent Diffusion Models for Fashion Image Editingcited by 5
  6. 2021"Are you sure?": Preliminary Insights from Scaling Product Comparisons to Multiple Shopscited by 4
  7. 2022"Does it come in black?" CLIP-like models are zero-shot recommenderscited by 4
  8. 2023ITEm: Unsupervised Image-Text Embedding Learning for eCommercecited by 0