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arXiv · 1805.08694

Image Based Fashion Product Recommendation with Deep Learning

Hessel Tuinhof·Clemens Pirker·Markus Haltmeier
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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.

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BibTeXRIS

Hessel Tuinhof, Clemens Pirker, Markus Haltmeier. 2018-05-06. Image Based Fashion Product Recommendation with Deep Learning. https://doi.org/10.1007/978-3-030-13709-0_40

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