# Learning fashion compatibility across apparel categories for outfit recommendation

## TL;DR

This paper proposes a novel deep learning approach using a siamese network with color features to improve fashion compatibility predictions for outfit recommendations.

## Contribution

It introduces a combined siamese and fully-connected network architecture with color features and a MAP training formulation for better fashion compatibility modeling.

## Key findings

- Enhanced compatibility prediction accuracy
- Effective use of color histogram features
- Sparse and correlated feature representations

## Abstract

This paper addresses the problem of generating recommendations for completing the outfit given that a user is interested in a particular apparel item. The proposed method is based on a siamese network used for feature extraction followed by a fully-connected network used for learning a fashion compatibility metric. The embeddings generated by the siamese network are augmented with color histogram features motivated by the important role that color plays in determining fashion compatibility. The training of the network is formulated as a maximum a posteriori (MAP) problem where Laplacian distributions are assumed for the filters of the siamese network to promote sparsity and matrix-variate normal distributions are assumed for the weights of the metric network to efficiently exploit correlations between the input units of each fully-connected layer.