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

arXiv: https://arxiv.org/abs/2107.03256 · HTML: https://mlsanity.com/paper/2107.03256/

## 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 (most cited first, 15 of 15)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Barlow Twins: Self-Supervised Learning via Redundancy Reduction](https://mlsanity.com/paper/2103.03230/) (2021)
- [Inferring Networks of Substitutable and Complementary Products](https://mlsanity.com/paper/1506.08839/) (2015)
- [Try This Instead: Personalized and Interpretable Substitute Recommendation](https://mlsanity.com/paper/2005.09344/) (2020)
- [AutoKnow: Self-Driving Knowledge Collection for Products of Thousands of Types](https://mlsanity.com/paper/2006.13473/) (2020)
- [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)
- [SIGIR 2021 E-Commerce Workshop Data Challenge](https://mlsanity.com/paper/2104.09423/) (2021)
- [Query2Prod2Vec Grounded Word Embeddings for eCommerce](https://mlsanity.com/paper/2104.02061/) (2021)
- [How to Grow a (Product) Tree: Personalized Category Suggestions for eCommerce Type-Ahead](https://mlsanity.com/paper/2005.12781/) (2020)
- [Recent Advances in Diversified Recommendation](https://mlsanity.com/paper/1905.06589/) (2019)
- [Learning Item-Interaction Embeddings for User Recommendations](https://mlsanity.com/paper/1812.04407/) (2018)
- [Deep Learning-based Online Alternative Product Recommendations at Scale](https://mlsanity.com/paper/2104.07572/) (2021)

## Built on by (most cited first, 4 of 4)

- [Beyond NDCG: behavioral testing of recommender systems with RecList](https://mlsanity.com/paper/2111.09963/) (2021)
- [DAG Card is the new Model Card](https://mlsanity.com/paper/2110.13601/) (2021)
- [Reasonable Scale Machine Learning with Open-Source Metaflow](https://mlsanity.com/paper/2303.11761/) (2023)
- ["Does it come in black?" CLIP-like models are zero-shot recommenders](https://mlsanity.com/paper/2204.02473/) (2022)

## 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.