Predicting User Demographics via Device Fingerprint Models
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Solution Overview
Problem
Social networking systems face reduced user interaction and revenue due to presenting irrelevant sponsored content, as they cannot select relevant content for users who are not logged in, limiting their ability to retrieve user-specific information.
Innovation Solution
The online system predicts user information by applying a model to interaction data, even when users are not logged in, using information from client devices and third-party systems, and trains models based on known user interactions to select content that matches targeting criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the online system requires users to be logged in to retrieve user-specific information for content selection, then the accuracy of content targeting is improved, but user interaction and revenue are reduced due to inability to present relevant content to non-logged-in users
Solution Approach 1:
The patent introduces an intermediary mechanism (device fingerprints, cookies, and prediction models) that bridges the gap between anonymous non-logged-in users and the content selection system. These intermediaries allow the system to infer user characteristics without requiring direct login authentication, thereby maintaining content relevance while expanding access to non-logged-in users.
Solution Approach 2:
The system creates a copy or proxy representation of user identity through device fingerprints and behavioral profiles. Instead of requiring the actual user account, the system generates a surrogate identity model that captures essential user characteristics for content matching purposes, enabling content delivery to non-logged-in users with targeted relevance.
2Adaptability or versatility
If the online system presents sponsored content to non-logged-in users without user-specific information, then user accessibility is improved, but content relevance deteriorates leading to reduced user interaction
Solution Approach 1:
The system performs preliminary actions by collecting and analyzing device fingerprints, browsing behavior, and interaction patterns before content delivery. This advance preparation enables the creation of predictive models that can estimate user demographics and interests even without login, allowing relevant content to be presented to non-logged-in users based on pre-analyzed behavioral data.
Solution Approach 2:
The patent changes the parameters used for content selection from explicit user-provided data (requiring login) to implicit behavioral and device-based parameters. By shifting from demographic fields filled by users to inferred characteristics based on device fingerprints and interaction patterns, the system maintains content relevance while removing the login barrier.
3Reliability
If the online system uses only logged-in user information for content selection, then data accuracy is improved, but system complexity increases due to authentication and data retrieval requirements
Solution Approach 1:
The patent extracts essential user identification and characterization elements from the complex authentication process. Instead of requiring full login sequences and access to complete user profiles, the system extracts key identifying features from device fingerprints and minimal interaction data, creating a streamlined content selection process that maintains accuracy while reducing complexity.
Data Source
AI summary
An online system using attributes of users to select content for presentation to the users predicts one or more attributes of users whose attributes are unavailable to the online system. For a user with one or more attributes unavailable to the online system, the online system applies a model to attributes of additional users to predict one or more attributes of the user. Attributes of the additional user use in the prediction may include demographic information and interactions with content by the additional users. The online system may determine an accuracy of the model by using the model to predict attributes for users whose attributes are known to the online system and comparing the predicted attributes to the known attributes. If the model's accuracy is less than a threshold value, the online system discontinues using the model to predict attributes of users.