This report describes our participation in the cDiscount 2015 challenge where the goal was to classify product items in a predefined taxonomy of products. Our best submission yielded an accuracy score of 64.20\% in the private part of the leaderboard and we were ranked 10th out of 175 participating teams. We followed a text classification approach employing mainly linear models. The final solution was a weighted voting system which combined a variety of trained models.
Cite
@article{arxiv.1606.02854,
title = {e-Commerce product classification: our participation at cDiscount 2015 challenge},
author = {Ioannis Partalas and Georgios Balikas},
journal= {arXiv preprint arXiv:1606.02854},
year = {2016}
}