Media Guidance Personalization Through Segmented Cross-Platform Behavior
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Solution Overview
Problem
Existing media guidance systems fail to effectively integrate and utilize user behavior data across multiple media platforms to personalize content recommendations and navigation, often relying solely on data from a single source or over-the-top services.
Innovation Solution
A system that modifies media guidance applications by analyzing user behavior across multiple platforms, determining relevance, and generating parameters to prioritize and visually distinguish content based on user preferences, platform relevance, and availability, while considering factors like device type, entitlements, and content expiration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If media guidance systems use data from multiple platforms to personalize recommendations, then recommendation accuracy improves, but system complexity increases
Solution Approach 1:
The patent segments user behavior data from different media platforms into distinct data sets, each processed and analyzed separately before being integrated. The system divides cross-platform data into platform-specific segments (broadcast television, streaming services, online video) and processes each segment through dedicated modules, reducing overall system complexity while maintaining recommendation accuracy.
Solution Approach 2:
The patent introduces intermediary components including a behavior data receiver that collects data from multiple platforms, a processing module that analyzes the data, and a recommendation generator that creates personalized suggestions. These intermediary elements mediate between raw multi-platform data and the final recommendation output, managing complexity through structured data flow and processing stages.
2Adaptability or versatility
If the system integrates behavior data from multiple sources, then personalization capability improves, but data processing time increases
Solution Approach 1:
The patent implements preliminary action by pre-processing and storing user behavior data from multiple platforms as it is collected, organizing it into structured formats before analysis is needed. The system pre-segments data by platform and user, creating ready-to-analyze data structures that reduce processing time when personalization recommendations are generated.
Solution Approach 2:
The patent applies parameter changes by transforming raw behavior data into standardized parameters and metrics that can be efficiently processed. The system converts diverse data formats from different platforms into uniform parameter structures, enabling faster comparison and analysis while maintaining the ability to personalize recommendations across multiple data sources.
Data Source
AI summary
Systems and methods are described for modifying a media guidance application. Such systems and methods may aid a user in selecting media content for viewing which may be of particular interest to them. Such systems and methods may receive programming information from one or more program guide sources, generate a media guidance application for display based upon the received programming information, receive behavior information from at least one further source, and generate parameters for modifying the media guidance application in response to the behavior information. The systems and methods may then modify the media guidance application based upon the generated parameters and display the modified media guidance application.