Media Guidance Application Predicting User Preferences via Detected Events
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Solution Overview
Problem
Conventional media guidance systems rely on past user behavior to update user profiles, resulting in profiles that better indicate previously preferred content rather than current or future preferences, as they do not account for real-time changes or events that may alter user preferences.
Innovation Solution
A media guidance application that predicts user preferences based on detected events, such as social media posts or real-life changes, by monitoring and updating user profiles to reflect current preferences and interests, ensuring consistency between user activities and preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If user profiles are updated based on past viewing behavior, then the profile reflects historical preferences, but it fails to capture current or future preferences that may have changed due to real-life events
Solution Approach 1:
The system proactively monitors social media platforms and external data sources for events that may indicate changing user preferences before these changes manifest in viewing behavior. By detecting events like engagements, pregnancies, or job changes early, the system updates profiles in advance, eliminating the time lag between real-life events and preference updates.
Solution Approach 2:
The system introduces social media data and external event sources as intermediary indicators of user preference changes. These intermediaries provide early signals about life events that may affect viewing preferences, allowing the system to predict and update profiles before actual viewing behavior changes occur.
2Measurement precision
If the system monitors social media and external events to predict preferences, then current and future preferences are captured, but system complexity increases
Solution Approach 1:
The system divides preference prediction into separate modules: one analyzing viewing behavior data and another monitoring social media and external events. Each module independently processes its data type and contributes to the overall profile, making the complex system more manageable and maintainable while improving prediction accuracy.
Solution Approach 2:
The system employs a universal event detection framework that monitors multiple data sources (social media platforms, news sources, user-generated content) using similar processing techniques. This multi-functional approach handles diverse data types through a unified architecture, reducing overall system complexity despite the breadth of monitoring.
3Measurement precision
If user profiles are dynamically updated based on detected events, then preference accuracy improves, but data processing requirements increase
Solution Approach 1:
The system monitors all user-generated content and social media events excessively to ensure no preference-changing event is missed. By applying partial updating—only modifying profile aspects directly affected by detected events rather than comprehensive re-analysis—the system maintains high accuracy while managing computational resources efficiently.
Data Source
AI summary
Methods and systems are disclosed herein for a media guidance application that predicts user preferences based on detected events. For example, in response to a trigger, the media guidance application may monitor data associated with a user. The media guidance application may then process the monitored data to determine if the monitored data indicates that a user profile of a user should be updated.