Context-Based User Identification for Digital Content Recommendations
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Solution Overview
Problem
Existing digital content distribution systems inaccurately recommend content due to lack of explicit user identification, as recommendations based on previous user actions can be inapplicable when multiple users interact or when a user's interests change.
Innovation Solution
A method that analyzes context information from a user's current session to infer properties and match them with predefined patterns of behavior, allowing for personalized content recommendations without explicit user identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If recommendations are based on previous user actions, then the system can provide personalized content suggestions, but the recommendations become inaccurate when multiple users interact with the same device or when a user's interests change
Solution Approach 1:
The system performs self-identification by automatically analyzing context information (device type, location, time, usage patterns) to infer user properties without requiring explicit user input. This resolves the contradiction by maintaining recommendation accuracy through automatic adaptation to different users while avoiding the need for users to manually identify themselves, thus preserving system versatility.
Solution Approach 2:
The system changes the parameters used for recommendation from static historical data to dynamic context-based inferred properties. By analyzing real-time context information and matching it against stored user profiles, the system adapts recommendations to the current user automatically, resolving the accuracy issue when multiple users share a device.
2Measurement precision
If the system requests explicit user identification, then it can accurately determine user identity and provide relevant recommendations, but it increases user burden and degrades user experience
Solution Approach 1:
The system performs self-identification by automatically analyzing context information (device type, location, time, usage patterns) to infer user properties without requiring explicit user input. This resolves the contradiction by maintaining recommendation accuracy through automatic adaptation to different users while avoiding the need for users to manually identify themselves, thus preserving system versatility.
Solution Approach 2:
The patent replaces the mechanical interaction of explicit user identification (users manually entering or selecting their identity) with an automated inference system that analyzes context information. This substitution eliminates the user burden while maintaining accurate user identity determination through contextual analysis and pattern matching.
3Measurement precision
If the system stores and analyzes detailed user behavior data, then it can improve recommendation accuracy, but it increases system complexity and data processing requirements
Solution Approach 1:
The system extracts only the essential context information parameters needed for user identification (device type, location, time, basic usage patterns) rather than storing and analyzing all possible user behavior data. This extraction approach maintains recommendation accuracy by focusing on the most discriminative features while reducing system complexity and data processing requirements.
Solution Approach 2:
The system performs preliminary organization of user profiles with predefined properties and characteristics before actual recommendation generation. By pre-structuring user data with key attributes and creating a framework for contextual matching, the system reduces the complexity of real-time analysis while maintaining high recommendation accuracy through efficient pattern comparison.
Data Source
AI summary
One embodiment of the present invention sets forth a technique for recommending digital content to a user of a digital content application based on continually learned patterns of behavior. Based on metrics collected from a current session of the digital content application, properties associated with one or more users interacting with the application are inferred. The inferred properties are matched against previously defined patterns of behavior to identify digital content that could be presented to the one or more users for optional selection.