Anonymous cross-device, cross-channel, and cross-venue user identification using adaptive deep learning

Abstract

Embodiments of the present invention provide systems, methods, and computer storage media for digital user identification across different devices, channels, and venues. Generally, digital interactions of a user can reveal a pattern of digital behavior that can be detected and assigned to the user, and a classifier can be learned to identify the user. Various types of digital interaction data may be utilized to identify a user, including device data, geolocation data associated with a user device, clickstream data or other attributes of web traffic, and the like. Anonymity can be provided by only utilizing behavioral-based user data. Digital interaction data can be encoded and fed into a multi-class classifier (e.g., deep neural network, support vector machine, random forest, k-nearest neighbors, etc.), with each user corresponding to a different class. New users can be detected and used to automatically grow a deep neural network to identify additional classes for the new users.

Dependencies are inferred from the claim text.