  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.
