# Toward Explainable Fashion Recommendation

## TL;DR

This paper introduces an explainable fashion recommendation system that not only scores outfits but also provides understandable reasons for its judgments by quantifying feature influence, enhancing user trust and system transparency.

## Contribution

It proposes a novel influence-based method to explain fashion recommendation scores by identifying key item-feature pairs without requiring human annotations.

## Key findings

- The method accurately detects items that lower outfit scores.
- Influence values effectively identify the most impactful features.
- The system improves interpretability of fashion recommendations.

## Abstract

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.