mlsanityData to 14 Oct 2026

"Are you sure?": Preliminary Insights from Scaling Product Comparisons to Multiple Shops

arXiv 2107.03256 ↗First posted 7 Jul 2021cs.IR cs.LGcited by 4

Not on the map. Why?

Abstract (arXiv)

Large eCommerce players introduced comparison tables as a new type of recommendations. However, building comparisons at scale without pre-existing training/taxonomy data remains an open challenge, especially within the operational constraints of shops in the long tail. We present preliminary results from building a comparison pipeline designed to scale in a multi-shop scenario: we describe our design choices and run extensive benchmarks on multiple shops to stress-test it. Finally, we run a small user study on property selection and conclude by discussing potential improvements and highlighting the questions that remain to be addressed.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 15 of its 43 references that are in the map. First posted on arXiv 7 Jul 2021.

Builds on

15 of 43 references.

References · 15

  1. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  2. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
  3. 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,375
  4. 2021Barlow Twins: Self-Supervised Learning via Redundancy Reductioncited by 1,838
  5. 2015Inferring Networks of Substitutable and Complementary Productscited by 92
  6. 2020Try This Instead: Personalized and Interpretable Substitute Recommendationcited by 34
  7. 2020AutoKnow: Self-Driving Knowledge Collection for Products of Thousands of Typescited by 31
  8. 2016E-commerce in Your Inbox: Product Recommendations at Scalecited by 28
  9. 2020Fantastic Embeddings and How to Align Them: Zero-Shot Inference in a Multi-Shop Scenariocited by 16
  10. 2021SIGIR 2021 E-Commerce Workshop Data Challengecited by 11
  11. 2021Query2Prod2Vec Grounded Word Embeddings for eCommercecited by 8
  12. 2020How to Grow a (Product) Tree: Personalized Category Suggestions for eCommerce Type-Aheadcited by 6
3 more
  1. 2019Recent Advances in Diversified Recommendationcited by 5
  2. 2018Learning Item-Interaction Embeddings for User Recommendationscited by 3
  3. 2021Deep Learning-based Online Alternative Product Recommendations at Scalecited by 3

Built on by

Cited by year

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

4 papers.