# Single-Item Fashion Recommender: Towards Cross-Domain Recommendations

arXiv: https://arxiv.org/abs/2111.00758 · HTML: https://mlsanity.com/paper/2111.00758/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 20 references that are in the map.
- First posted on arXiv 1 Nov 2021.

## Builds on (most cited first, 10 of 10)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback](https://mlsanity.com/paper/1510.01784/) (2015)
- [Visually-Aware Fashion Recommendation and Design with Generative Image Models](https://mlsanity.com/paper/1711.02231/) (2017)
- [Deep Learning based Large Scale Visual Recommendation and Search for E-Commerce](https://mlsanity.com/paper/1703.02344/) (2017)
- [MMFashion: An Open-Source Toolbox for Visual Fashion Analysis](https://mlsanity.com/paper/2005.08847/) (2020)
- [Image Based Fashion Product Recommendation with Deep Learning](https://mlsanity.com/paper/1805.08694/) (2018)
- [Buy Me That Look: An Approach for Recommending Similar Fashion Products](https://mlsanity.com/paper/2008.11638/) (2020)
- [Algorithmic clothing: hybrid recommendation, from street-style-to-shop](https://mlsanity.com/paper/1705.09451/) (2017)
- [K-Splits: Improved K-Means Clustering Algorithm to Automatically Detect the Number of Clusters](https://mlsanity.com/paper/2110.04660/) (2021)

## Abstract (arXiv)

Nowadays, recommender systems and search engines play an integral role in fashion e-commerce. Still, many challenges lie ahead, and this study tries to tackle some. This article first suggests a content-based fashion recommender system that uses a parallel neural network to take a single fashion item shop image as input and make in-shop recommendations by listing similar items available in the store. Next, the same structure is enhanced to personalize the results based on user preferences. This work then introduces a background augmentation technique that makes the system more robust to out-of-domain queries, enabling it to make street-to-shop recommendations using only a training set of catalog shop images. Moreover, the last contribution of this paper is a new evaluation metric for recommendation tasks called objective-guided human score. This method is an entirely customizable framework that produces interpretable, comparable scores from subjective evaluations of human scorers.