Cross-Device Tracking: Matching Devices and Cookies
This addresses identity fragmentation for applications in a multi-device world, but it is incremental as it builds on existing methods for a specific challenge.
The paper tackled the problem of cross-device user identification by matching cookies to individuals using semi-supervised machine learning, achieving third place in the ICDM 2015 Drawbridge challenge.
The number of computers, tablets and smartphones is increasing rapidly, which entails the ownership and use of multiple devices to perform online tasks. As people move across devices to complete these tasks, their identities becomes fragmented. Understanding the usage and transition between those devices is essential to develop efficient applications in a multi-device world. In this paper we present a solution to deal with the cross-device identification of users based on semi-supervised machine learning methods to identify which cookies belong to an individual using a device. The method proposed in this paper scored third in the ICDM 2015 Drawbridge Cross-Device Connections challenge proving its good performance.