  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.
