End-to-End Image-Based Fashion Recommendation

TL;DR

This paper introduces a simple attribute-aware recommendation model that effectively incorporates item image features using ResNet50, significantly improving performance over existing image-based recommenders.

Contribution

The paper proposes a novel attribute-aware model that seamlessly integrates image features into recommendation systems, outperforming state-of-the-art methods on real-world datasets.

Findings

The model significantly outperforms existing image-based recommenders.

Incorporating image features via the proposed method improves recommendation accuracy.

The ablation study demonstrates the effectiveness of different feature integration techniques.

Abstract

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.