Image Based Fashion Product Recommendation with Deep Learning
This is an incremental improvement for fashion e-commerce platforms, combining deep learning with traditional methods to enhance recommendation accuracy.
The authors tackled fashion product recommendation by developing a two-stage deep learning framework that uses a neural network classifier as a feature extractor and a ranking algorithm for similarity-based recommendations, achieving improved robustness and performance in matching customer styles.
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.