# End-to-End Image-Based Fashion Recommendation

arXiv: https://arxiv.org/abs/2205.02923 · HTML: https://mlsanity.com/paper/2205.02923/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 16 references that are in the map.
- First posted on arXiv 5 May 2022.

## Builds on (most cited first, 6 of 6)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Image-based Recommendations on Styles and Substitutes](https://mlsanity.com/paper/1506.04757/) (2015)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [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)
- [Explainable Fashion Recommendation: A Semantic Attribute Region Guided Approach](https://mlsanity.com/paper/1905.12862/) (2019)

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

- [IISAN: Efficiently Adapting Multimodal Representation for Sequential Recommendation with Decoupled PEFT](https://mlsanity.com/paper/2404.02059/) (2024)
- [Enhancing Taobao Display Advertising with Multimodal Representations: Challenges, Approaches and Insights](https://mlsanity.com/paper/2407.19467/) (2024)
- [Efficient and Effective Adaptation of Multimodal Foundation Models in Sequential Recommendation](https://mlsanity.com/paper/2411.02992/) (2024)
- [Modeling Bias Evolution in Fashion Recommender Systems: A System Dynamics Approach](https://mlsanity.com/paper/2510.21728/) (2025)
- [Rethinking Convolutional Networks for Attribute-Aware Sequential Recommendation](https://mlsanity.com/paper/2605.04723/) (2026)

## Abstract (arXiv)

In fashion-based recommendation settings, incorporating the item image features is considered a crucial factor, and it has shown significant improvements to many traditional models, including but not limited to matrix factorization, auto-encoders, and nearest neighbor models. While there are numerous image-based recommender approaches that utilize dedicated deep neural networks, comparisons to attribute-aware models are often disregarded despite their ability to be easily extended to leverage items' image features. In this paper, we propose a simple yet effective attribute-aware model that incorporates image features for better item representation learning in item recommendation tasks. The proposed model utilizes items' image features extracted by a calibrated ResNet50 component. We present an ablation study to compare incorporating the image features using three different techniques into the recommender system component that can seamlessly leverage any available items' attributes. Experiments on two image-based real-world recommender systems datasets show that the proposed model significantly outperforms all state-of-the-art image-based models.