# Toward Explainable Fashion Recommendation

arXiv: https://arxiv.org/abs/1901.04870 · HTML: https://mlsanity.com/paper/1901.04870/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jan 2019.

## Built on by (most cited first, 6 of 6)

- [Semi-Supervised Visual Representation Learning for Fashion Compatibility](https://mlsanity.com/paper/2109.08052/) (2021)
- [Smart Fashion: A Review of AI Applications in the Fashion & Apparel Industry](https://mlsanity.com/paper/2111.00905/) (2021)
- [Deciphering Compatibility Relationships with Textual Descriptions via Extraction and Explanation](https://mlsanity.com/paper/2312.11554/) (2023)
- [FashionM3: Multimodal, Multitask, and Multiround Fashion Assistant based on Unified Vision-Language Model](https://mlsanity.com/paper/2504.17826/) (2025)
- [fAshIon after fashion: A Report of AI in Fashion](https://mlsanity.com/paper/2105.03050/) (2021)
- [Fashion Object Detection for Tops & Bottoms](https://mlsanity.com/paper/2305.18482/) (2023)

## Abstract (arXiv)

Many studies have been conducted so far to build systems for recommending fashion items and outfits. Although they achieve good performances in their respective tasks, most of them cannot explain their judgments to the users, which compromises their usefulness. Toward explainable fashion recommendation, this study proposes a system that is able not only to provide a goodness score for an outfit but also to explain the score by providing reason behind it. For this purpose, we propose a method for quantifying how influential each feature of each item is to the score. Using this influence value, we can identify which item and what feature make the outfit good or bad. We represent the image of each item with a combination of human-interpretable features, and thereby the identification of the most influential item-feature pair gives useful explanation of the output score. To evaluate the performance of this approach, we design an experiment that can be performed without human annotation; we replace a single item-feature pair in an outfit so that the score will decrease, and then we test if the proposed method can detect the replaced item correctly using the above influence values. The experimental results show that the proposed method can accurately detect bad items in outfits lowering their scores.