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 | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-030-13709-0_40 | LOD |
DocType | ISSN | Citations |
Conference | LOD: International Conference on Machine Learning, Optimization,
and Data Science Machine Learning, Optimization, and Data Science 4th
International Conference, LOD 2018, Volterra, Italy, September 13-16, 2018,
Revised Selected Papers | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hessel Tuinhof | 1 | 0 | 0.34 |
| Clemens Pirker | 2 | 0 | 0.68 |
| Markus Haltmeier | 3 | 4 | 2.48 |