Aesthetic-based Clothing Recommendation

TL;DR

This paper introduces a novel clothing recommendation approach that incorporates aesthetic features extracted by a neural network, enabling personalized suggestions that align with individual aesthetic preferences and outperform existing methods.

Contribution

It proposes integrating aesthetic features into clothing recommendation systems using a neural network and a tensor factorization model for personalization.

Findings

The method significantly outperforms state-of-the-art recommendation techniques.

Aesthetic features improve the personalization of clothing recommendations.

The approach effectively captures user aesthetic preferences.

Abstract

Recently, product images have gained increasing attention in clothing recommendation since the visual appearance of clothing products has a significant impact on consumers' decision. Most existing methods rely on conventional features to represent an image, such as the visual features extracted by convolutional neural networks (CNN features) and the scale-invariant feature transform algorithm (SIFT features), color histograms, and so on. Nevertheless, one important type of features, the \emph{aesthetic features}, is seldom considered. It plays a vital role in clothing recommendation since a users' decision depends largely on whether the clothing is in line with her aesthetics, however the conventional image features cannot portray this directly. To bridge this gap, we propose to introduce the aesthetic information, which is highly relevant with user preference, into clothing recommender systems. To achieve this, we first present the aesthetic features extracted by a pre-trained neural network, which is a brain-inspired deep structure trained for the aesthetic assessment task. Considering that the aesthetic preference varies significantly from user to user and by time, we then propose a new tensor factorization model to incorporate the aesthetic features in a personalized manner. We conduct extensive experiments on real-world datasets, which demonstrate that our approach can capture the aesthetic preference of users and significantly outperform several state-of-the-art recommendation methods.