Siamese Cookie Embedding Networks for Cross-Device User Matching

Over the last decade, the number of devices per person has increased substantially. Œis 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 seŠing and introduce the Siamese Cookie EmbeddingNetwork (SCEmNet), a siamese convolutional architecture that leverages the multi-modal aspect of sequences, and show significant improvement over the state-of-the-art.