Over the last decade, the number of devices per person has increased substantially. This poses a challenge for cookie-based personalization applications, such as online search and advertising, as it narrows the personalization signal to a single device environment. A key task is to find which cookies belong to the same person to recover a complete cross-device user journey. Recent work on the topic has shown the benefits of using unsupervised embeddings learned on user event sequences. In this paper, we extend this approach to a supervised setting and introduce the Siamese Cookie Embedding Network (SCEmNet), a siamese convolutional architecture that leverages the multi-modal aspect of sequences, and show significant improvement over the state-of-the-art.
@article{arxiv.1803.10450,
title = {Siamese Cookie Embedding Networks for Cross-Device User Matching},
author = {Ugo Tanielian and Anne-Marie Tousch and Flavian Vasile},
journal= {arXiv preprint arXiv:1803.10450},
year = {2018}
}