# Large Scale Visual Recommendations From Street Fashion Images

arXiv: https://arxiv.org/abs/1401.1778 · HTML: https://mlsanity.com/paper/1401.1778/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jan 2014.

## Built on by (most cited first, 7 of 7)

- [Adversarial Item Promotion on Visually-Aware Recommender Systems by Guided Diffusion](https://mlsanity.com/paper/2312.15826/) (2023)
- [Smart Fashion: A Review of AI Applications in the Fashion & Apparel Industry](https://mlsanity.com/paper/2111.00905/) (2021)
- [fAshIon after fashion: A Report of AI in Fashion](https://mlsanity.com/paper/2105.03050/) (2021)
- [AI Recommendation System for Enhanced Customer Experience: A Novel Image-to-Text Method](https://mlsanity.com/paper/2311.09624/) (2023)
- [Color Variants Identification in Fashion e-commerce via Contrastive Self-Supervised Representation Learning](https://mlsanity.com/paper/2104.08581/) (2021)
- [A Tale of Color Variants: Representation and Self-Supervised Learning in Fashion E-Commerce](https://mlsanity.com/paper/2112.02910/) (2021)
- [Fashion CUT: Unsupervised domain adaptation for visual pattern classification in clothes using synthetic data and pseudo-labels](https://mlsanity.com/paper/2305.05580/) (2023)

## Abstract (arXiv)

We describe a completely automated large scale visual recommendation system for fashion. Our focus is to efficiently harness the availability of large quantities of online fashion images and their rich meta-data. Specifically, we propose four data driven models in the form of Complementary Nearest Neighbor Consensus, Gaussian Mixture Models, Texture Agnostic Retrieval and Markov Chain LDA for solving this problem. We analyze relative merits and pitfalls of these algorithms through extensive experimentation on a large-scale data set and baseline them against existing ideas from color science. We also illustrate key fashion insights learned through these experiments and show how they can be employed to design better recommendation systems. Finally, we also outline a large-scale annotated data set of fashion images (Fashion-136K) that can be exploited for future vision research.