Similarity Learning for Cookie-Based Cross-Device Attribution
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Solution Overview
Problem
Existing methods for attributing browsing activity across multiple devices to a single user are inadequate, as cookies are device-specific and do not uniquely identify the user, leading to deficiencies in providing personalized content.
Innovation Solution
A method and system using a Gaussian mixture model and a random forest classifier to analyze cookie characteristics across devices, determining the probability that cookies from different devices belong to the same user, allowing for personalized content delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If cookies are used to track browsing behavior, then user interests can be determined, but the user cannot be uniquely identified across multiple devices
Solution Approach 1:
The patent introduces device fingerprints as intermediary identifiers that capture unique characteristics of devices (screen resolution, device model, operating system version) to bridge the gap between device-specific cookies and user identification. These fingerprints serve as mediators that can be shared across devices to link browsing sessions to the same user without requiring direct user authentication.
Solution Approach 2:
The patent transforms the tracking approach by changing from relying solely on device-specific cookies to using a combination of device fingerprints and cookie data. By extracting and analyzing multiple parameters from device characteristics and browsing behavior, the system creates a more robust identification framework that works across different devices.
2Productivity
If device-specific cookies are used for tracking, then browsing activity can be recorded, but personalized content cannot be provided when users switch devices
Solution Approach 1:
The patent merges device fingerprint data with cookie information to create a unified user profile system. By combining the persistent nature of cookies with the cross-device capability of device fingerprints, the system maintains user profile continuity across multiple devices, enabling personalized content delivery regardless of which device the user employs.
Solution Approach 2:
The patent performs preliminary extraction and storage of device fingerprint characteristics when a user first interacts with the system. This preliminary action creates a foundation for future cross-device recognition, allowing the system to quickly identify and personalize content for returning users across different devices without requiring re-authentication.
3Measurement precision
If machine learning models are applied to analyze cookie characteristics, then cross-device attribution accuracy improves, but system complexity increases
Solution Approach 1:
The patent segments the attribution system into distinct functional components: device fingerprint extraction module, feature engineering module, machine learning classification module, and personalization delivery module. This segmentation allows each component to be developed, tested, and optimized independently, reducing overall system complexity while maintaining high attribution accuracy through specialized processing at each stage.
Data Source
AI summary
Methods and systems for attributing browsing activity from two or more different network-connected devices to a single user are disclosed. In one aspect, cookies generated by the browsing activity of different unidentified devices at a website are received. A random forest classifier trained on probabilities output from a Gaussian mixture model is applied to the unidentified cookies to determine a probability that two different cookies were generated by the same user. In some embodiments, personalized content is then delivered to the user based on the characteristics of the paired cookies.