  In fashion-based recommendation settings, incorporating the item image
features is considered a crucial factor, and it has shown significant
improvements to many traditional models, including but not limited to matrix
factorization, auto-encoders, and nearest neighbor models. While there are
numerous image-based recommender approaches that utilize dedicated deep neural
networks, comparisons to attribute-aware models are often disregarded despite
their ability to be easily extended to leverage items' image features. In this
paper, we propose a simple yet effective attribute-aware model that
incorporates image features for better item representation learning in item
recommendation tasks. The proposed model utilizes items' image features
extracted by a calibrated ResNet50 component. We present an ablation study to
compare incorporating the image features using three different techniques into
the recommender system component that can seamlessly leverage any available
items' attributes. Experiments on two image-based real-world recommender
systems datasets show that the proposed model significantly outperforms all
state-of-the-art image-based models.
