Object detection and multimodal learning for product recommendations

Abstract

This study showcases how deep learning can be applied to automated information extraction in fashion data to create a recommendation system. The proposed approach is an algorithm for recommending multiple products based on visual and textual features, ensuring compatibility with query items. The object detection model can detect many products across different garment categories. The study utilized public e-commerce datasets and trained models using deep learning methods. The compatibility model has shown promising results in automating recommendations of compatible products based on user interests. The study experimented with multiple pre-trained feature extraction models and successfully trained the object detection model for fashion article detection and localization task. Overall, the goal is to deploy the method to enhance its effectiveness and usefulness in providing a satisfying shopping experience for e-commerce users.