Cross-Platform Media Guidance Personalization With Weighted Behavior Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing media guidance systems are limited to over-the-top platforms and do not effectively integrate user behavior data across multiple media environments, failing to provide personalized recommendations based on diverse media consumption habits.
Innovation Solution
A media guidance application that modifies its content presentation and recommendations based on user behavior data from multiple platforms, using a recommendation model with weightings and considering factors like platform relevance, content availability, and user entitlements to enhance personalization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If media guidance systems integrate user behavior data from multiple platforms, then personalization and user experience are improved, but system complexity and data processing requirements increase
Solution Approach 1:
The patent segments user behavior data from different media platforms (broadcast television, cable television, satellite television, internet protocol television, on-demand media, streaming media) into distinct data sources, processing each platform's data independently before integration. This segmentation allows the system to handle multiple platform types without overwhelming complexity, as each data stream can be processed using platform-specific protocols while contributing to a unified user profile.
Solution Approach 2:
The patent implements a universal media guidance application that functions across multiple media platforms simultaneously. The system uses a standardized data structure and processing framework that can accommodate different platform types, enabling the same core functionality to serve diverse data sources. This multi-functionality approach allows the system to integrate various platforms without requiring separate systems for each, thereby improving personalization while controlling complexity through code reuse and standardized interfaces.
2Measurement precision
If the system analyzes behavior information using recommendation models with weightings, then content recommendation accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The patent applies preliminary weighting to behavior information based on predetermined factors before full analysis. By pre-establishing weightings for different types of behavior data (e.g., giving higher weight to recently consumed content or to content from preferred platforms), the system prepares recommendation models in advance, reducing the computational burden during real-time recommendation generation and thereby decreasing processing time while maintaining accuracy.
Solution Approach 2:
The patent dynamically adjusts parameters in the recommendation model, such as weighting factors and threshold values, based on user behavior patterns and system performance metrics. By changing these parameters adaptively, the system optimizes the balance between recommendation accuracy and processing efficiency, allowing it to achieve high precision without always requiring maximum computational resources.
3Ease of operation
If the system modifies program listings based on user behavior data, then content relevance and user engagement are improved, but data privacy concerns and security requirements increase
Solution Approach 1:
The patent extracts and processes only the necessary behavior data elements required for modification of program listings, rather than collecting or storing complete user profiles. By extracting only the specific data points needed (such as content consumption patterns, platform preferences, and viewing times) and discarding or anonymizing other information, the system reduces data privacy risks while still achieving personalized content recommendations and improved user engagement.
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.