# Image Based Fashion Product Recommendation with Deep Learning

arXiv: https://arxiv.org/abs/1805.08694 · HTML: https://mlsanity.com/paper/1805.08694/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 May 2018.

## Built on by (most cited first, 5 of 5)

- [Smart Fashion: A Review of AI Applications in the Fashion & Apparel Industry](https://mlsanity.com/paper/2111.00905/) (2021)
- [Human Body Shape Classification Based on a Single Image](https://mlsanity.com/paper/2305.18480/) (2023)
- [Single-Item Fashion Recommender: Towards Cross-Domain Recommendations](https://mlsanity.com/paper/2111.00758/) (2021)
- [Predicting and Understanding Human Action Decisions during Skillful Joint-Action via Machine Learning and Explainable-AI](https://mlsanity.com/paper/2206.02739/) (2022)
- [Data-driven intelligent computational design for products: Method, techniques, and applications](https://mlsanity.com/paper/2301.12382/) (2023)

## Abstract (arXiv)

We develop a two-stage deep learning framework that recommends fashion images based on other input images of similar style. For that purpose, a neural network classifier is used as a data-driven, visually-aware feature extractor. The latter then serves as input for similarity-based recommendations using a ranking algorithm. Our approach is tested on the publicly available Fashion dataset. Initialization strategies using transfer learning from larger product databases are presented. Combined with more traditional content-based recommendation systems, our framework can help to increase robustness and performance, for example, by better matching a particular customer style.