Name
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. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Computer Vision and Pattern Recognition | Recommender system,Ranking,Computer science,Convolutional neural network,Transfer of learning,Image based,Robustness (computer science),Artificial intelligence,Initialization,Deep learning,Machine learning |
DocType | Volume | Citations |
Journal | abs/1805.08694 | 0 |
PageRank | References | Authors |
0.34 | 10 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hessel Tuinhof | 1 | 0 | 0.34 |
| Clemens Pirker | 2 | 0 | 0.34 |
| Markus Haltmeier | 3 | 74 | 14.16 |