An Efficient Fashion-Driven Learning Approach to Model User Preferences in On-Line Shopping Scenarios
Abstract
In this work we tackle the problem of search personalization for on-line soft goods shopping. By learning what the user likes and what the user does not like, better search rankings and therefore a better overall shopping experience can be obtained. The first contribution of the work is in terms of feature selection: given the specific nature of the domain, we combine the traditional visual and text feature into a fashion-driven low dimensional space, compact yet very discriminative. On the learning stage, we describe a two step hybrid learning algorithm, that combines a discriminative model learned off-line over historical data, with an extremely efficient generative model, updated on-line according to the user behavior. Qualitative and quantitative analyses show promising results.
Cite
Text
Çamoglu et al. "An Efficient Fashion-Driven Learning Approach to Model User Preferences in On-Line Shopping Scenarios." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2010. doi:10.1109/CVPRW.2010.5543748Markdown
[Çamoglu et al. "An Efficient Fashion-Driven Learning Approach to Model User Preferences in On-Line Shopping Scenarios." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2010.](https://mlanthology.org/cvprw/2010/camoglu2010cvprw-efficient/) doi:10.1109/CVPRW.2010.5543748BibTeX
@inproceedings{camoglu2010cvprw-efficient,
title = {{An Efficient Fashion-Driven Learning Approach to Model User Preferences in On-Line Shopping Scenarios}},
author = {Çamoglu, Orhan and Yu, Tian-Li and Bertelli, Luca and Vu, Diem and V, Muralidharan and Gokturk, Salih},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2010},
pages = {28-34},
doi = {10.1109/CVPRW.2010.5543748},
url = {https://mlanthology.org/cvprw/2010/camoglu2010cvprw-efficient/}
}