# Dressing as a Whole: Outfit Compatibility Learning Based on Node-wise Graph Neural Networks

## TL;DR

This paper introduces a novel graph neural network approach to model complex relationships among fashion items for outfit compatibility prediction, outperforming previous methods by representing outfits as graphs and using node-wise interactions.

## Contribution

The paper proposes Node-wise Graph Neural Networks (NGNN) that effectively model outfit compatibility through a graph-based representation, capturing complex item interactions.

## Key findings

- NGNN outperforms existing methods in compatibility prediction.
- The approach effectively models multi-modal outfit data.
- Experimental results validate the superiority of NGNN.

## Abstract

With the rapid development of fashion market, the customers' demands of customers for fashion recommendation are rising. In this paper, we aim to investigate a practical problem of fashion recommendation by answering the question "which item should we select to match with the given fashion items and form a compatible outfit". The key to this problem is to estimate the outfit compatibility. Previous works which focus on the compatibility of two items or represent an outfit as a sequence fail to make full use of the complex relations among items in an outfit. To remedy this, we propose to represent an outfit as a graph. In particular, we construct a Fashion Graph, where each node represents a category and each edge represents interaction between two categories. Accordingly, each outfit can be represented as a subgraph by putting items into their corresponding category nodes. To infer the outfit compatibility from such a graph, we propose Node-wise Graph Neural Networks (NGNN) which can better model node interactions and learn better node representations. In NGNN, the node interaction on each edge is different, which is determined by parameters correlated to the two connected nodes. An attention mechanism is utilized to calculate the outfit compatibility score with learned node representations. NGNN can not only be used to model outfit compatibility from visual or textual modality but also from multiple modalities. We conduct experiments on two tasks: (1) Fill-in-the-blank: suggesting an item that matches with existing components of outfit; (2) Compatibility prediction: predicting the compatibility scores of given outfits. Experimental results demonstrate the great superiority of our proposed method over others.