Image Based Fashion Product Recommendation with Deep Learning

TL;DR

This paper presents a deep learning framework for fashion image recommendation that combines visual feature extraction with similarity ranking, improving robustness and style matching in fashion recommendations.

Contribution

Introduces a two-stage deep learning approach with transfer learning for improved fashion image recommendation and integration with traditional systems.

Findings

Effective visual feature extraction using neural networks

Enhanced recommendation accuracy with transfer learning

Improved style matching in fashion recommendations

Abstract

We develop a two-stage deep learning framework that recommends fashion images based on other input images of similar style. For that purpose, a neural network classifier is used as a data-driven, visually-aware feature extractor. The latter then serves as input for similarity-based recommendations using a ranking algorithm. Our approach is tested on the publicly available Fashion dataset. Initialization strategies using transfer learning from larger product databases are presented. Combined with more traditional content-based recommendation systems, our framework can help to increase robustness and performance, for example, by better matching a particular customer style.