"Are you sure?": Preliminary Insights from Scaling Product Comparisons to Multiple Shops
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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
- 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
- 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
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- 2021Barlow Twins: Self-Supervised Learning via Redundancy Reductioncited by 1,838
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- 2020Fantastic Embeddings and How to Align Them: Zero-Shot Inference in a Multi-Shop Scenariocited by 16
- 2021SIGIR 2021 E-Commerce Workshop Data Challengecited by 11
- 2021Query2Prod2Vec Grounded Word Embeddings for eCommercecited by 8
- 2020How to Grow a (Product) Tree: Personalized Category Suggestions for eCommerce Type-Aheadcited by 6
3 more
Built on by
Citing papers per year: 2021: 2; 2022: 1; 2023: 1.
4 papers.
- 2021Beyond NDCG: behavioral testing of recommender systems with RecListcited by 8
- 2021DAG Card is the new Model Cardcited by 5
- 2023Reasonable Scale Machine Learning with Open-Source Metaflowcited by 5
- 2022"Does it come in black?" CLIP-like models are zero-shot recommenderscited by 4
- 2023Reasonable Scale Machine Learning with Open-Source Metaflow21 Mar 2023
- 2022"Does it come in black?" CLIP-like models are zero-shot recommenders5 Apr 2022
- 2021Beyond NDCG: behavioral testing of recommender systems with RecList18 Nov 2021
- 2021DAG Card is the new Model Card24 Oct 2021