In this paper, we propose two methods for tackling the problem of cross-device matching for online advertising at CIKM Cup 2016. The first method considers the matching problem as a binary classification task and solve it by utilizing ensemble learning techniques. The second method defines the matching problem as a ranking task and effectively solve it with using learning-to-rank algorithms. The results show that the proposed methods obtain promising results, in which the ranking-based method outperforms the classification-based method for the task.
@article{arxiv.1612.07117,
title = {Classification and Learning-to-rank Approaches for Cross-Device Matching at CIKM Cup 2016},
author = {Nam Khanh Tran},
journal= {arXiv preprint arXiv:1612.07117},
year = {2016}
}