Personalized News Program Generation via User Behavior Analysis
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Solution Overview
Problem
Traditional news programs are not personalized to individual user preferences, leading to a decline in television audiences as people increasingly consume news through mobile platforms and social media, necessitating improved systems for generating and delivering on-demand, personalized news content.
Innovation Solution
A system that uses user behavior analysis to create personalized news programs by applying explicit and implicit constraints, incorporating editorial curation and user feedback, to select and stream news segments tailored to individual interests, preferences, and viewing habits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional mass broadcast news programs are used, then content delivery is simple and standardized, but user engagement declines and audiences decrease due to lack of personalization
Solution Approach 1:
The news program is segmented into multiple independent components including news segments, advertisement segments, and filler segments. Each segment type can be independently selected and assembled based on user preferences and constraints, enabling personalized news programs while maintaining manageable system complexity through modular content organization
Solution Approach 2:
The system dynamically adjusts news program composition based on user behavior analysis, explicit constraints, and implicit constraints. The program module continuously learns from user interactions (swipe gestures, viewing patterns) and adapts content selection in real-time, allowing the system to provide personalized news programs without requiring complex manual configuration
2Measurement precision
If user behavior analysis and multiple constraints are applied for personalization, then news program relevance to user interests improves, but content selection complexity increases
Solution Approach 1:
The system implements continuous feedback loops where user interactions (swipe gestures, viewing completion, skip actions) are analyzed to refine implicit constraints. This feedback mechanism progressively improves user preference accuracy by learning from actual behavior patterns, while the automated learning process manages content selection complexity through data-driven decisions rather than manual rule management
Solution Approach 2:
The system changes multiple parameters simultaneously including explicit constraints (duration, geography, genre) and implicit constraints (viewing history, interaction patterns). By coordinating changes across these parameters through the program module, the system achieves precise user preference matching while managing overall selection complexity through integrated parameter optimization
3Reliability
If editorial curation metadata is incorporated into content selection, then news quality and reliability improve, but processing requirements increase
Solution Approach 1:
Editorial curation metadata is pre-computed and attached to content during the content creation and ingestion phase. This preliminary action allows the system to leverage pre-processed quality indicators during news program assembly, improving news reliability while minimizing real-time computational energy consumption by avoiding redundant analysis of content quality
4Ease of operation
If on-demand personalized news programs are generated and streamed, then user engagement and satisfaction improve, but system resource requirements increase
Solution Approach 1:
The system enables users to access personalized news programs on-demand through mobile devices without requiring complex manual configuration. Users simply interact with the interface (swipe gestures, selections) and the system automatically generates and streams personalized content, improving ease of operation while the automated generation process manages infrastructure complexity through efficient resource utilization
Data Source
AI summary
Systems and technologies for providing an on-demand personalized news program are disclosed. The system and technologies allow an end user to create a news program that best matches the end user's tastes and preferences. To achieve this, one aspect of the disclosure relates to systematic personalization. Systematic personalization involves determining an end user's interests based on an assessment of user behavior as it relates to accessible content. The personalization process may also provide content to users that have been editorially curated.