Social Contact Program Ranking System
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Solution Overview
Problem
Current systems lack an effective way to rank and provide program content based on the viewing habits of a user's social contacts, failing to enhance the user's viewing experience by integrating social interactions with traditional television viewing.
Innovation Solution
A method and system that store user profile data and associate user identifiers with their social contacts' identifiers, receive program identifier data, rank it based on user profile data, and display it in a user-friendly format, allowing users to select and access programs viewed by their contacts, even on legacy television modules with internet connectivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If program content is ranked based on social contacts' viewing habits, then user engagement and content discovery are improved, but system complexity and data processing requirements increase
Solution Approach 1:
The system segments the complex task of program recommendation by separating data collection (from social contacts), data processing (ranking algorithms), and presentation (program listing) into distinct modular components. This allows the ranking function to be implemented independently without overwhelming system complexity.
Solution Approach 2:
The patent introduces an intermediary ranking system that mediates between raw social viewing data and the final program presentation. This intermediary layer processes and filters social contacts' viewing habits through user profile data, transforming complex social data into simplified ranked recommendations.
2Loss of information
If user profile data and social contact associations are stored and processed, then personalized program recommendations are improved, but data storage and processing requirements increase
Solution Approach 1:
The system applies local quality by storing and processing only the specific user profile data and social contact associations relevant to program viewing behavior, rather than maintaining all possible user data. This selective data retention optimizes storage while maintaining recommendation quality.
Solution Approach 2:
User profile data and social contact associations are stored and pre-processed in advance, creating a ready-to-use data foundation before program ranking is needed. This preliminary data preparation reduces real-time processing requirements and enables faster recommendation generation.
3Measurement precision
If programs are ranked based on social interactions, then program selection accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The system implements partial action by ranking programs based on a selective subset of social contacts' viewing habits and user profile data, rather than processing all possible data sources. This approach achieves sufficient recommendation accuracy without requiring exhaustive data processing.
Solution Approach 2:
The patent employs parameter changes by dynamically adjusting the weighting and relevance of different user profile attributes and social contact relationships in the ranking algorithm. This allows the system to optimize between accuracy and processing time by modifying ranking parameters based on available data and computational constraints.
Data Source
AI summary
Systems and methods for providing a program listing include storing user profile data and a user identifier for a user; storing an association of the user identifier with user identifiers for each of the plurality of social contacts of the user; receiving program identifier data representing programs currently being viewed by the social contacts; ranking the program identifier data for each of the plurality of social contacts based at least in part on the user profile data; and sending display data representing the program identifier data for display in an order based on the ranking.