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XRec: Behavior-Based User Recognition Across Mobile Devices

As smartphones and tablets become increasingly prevalent, more customers have multiple devices. The multi-user, multi-device interactions inspire many problems worthy of investigation, among which recognizing users across devices has significant implications on recommendation, advertising and user experience. Unlike the binary classification problem in user identification on a single device, cross-device user recognition is essentially a set partition problem. The app back-end aims to divide user activities on devices hosting the app into groups each associated with one user. In this paper, we present XRec which leverages user behavioral patterns, namely when, where and how a user uses the app, to achieve the recognition. To address the user-device partition problem, we propose a classification-plus-refinement algorithm. To validate our approach, we conduct a field study with an Android app. We instrument the app to collect usage data from real users. We provide proof-of-concept experimental results to demonstrate how XRec can provide added value to mobile apps, with the ability to correctly match a user across multiple devices with 70% recall and 90% precision.