Image Based Fashion Product Recommendation With Deep Learning
Hessel Tuinhof, Clemens Pirker, Markus Haltmeier . Lecture Notes in Computer Science 2018 – 50 citations
[Paper]
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.