Personalized TV Channel Scheduling for Faster Content Selection
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Solution Overview
Problem
Users face challenges in navigating and selecting audiovisual content that aligns with their personal preferences due to the vast and varied offerings from television channels and platforms, leading to inefficiencies in finding content that meets their specific tastes and expectations.
Innovation Solution
A method and system for generating a personalized television channel using machine learning to analyze user preferences and content characteristics, selecting and scheduling audiovisual content that aligns with user criteria, and providing access information for playback on a user's terminal.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If users navigate through multiple channels to find content matching their preferences, then they can access diverse audiovisual content, but the time and effort required to select appropriate content increases significantly
Solution Approach 1:
The system performs preliminary analysis of user preferences and content characteristics before the user needs to select content. Machine learning models pre-process and match user profiles with available content, preparing personalized recommendations in advance so users can immediately access relevant content without manual searching.
Solution Approach 2:
The system enables self-service by automatically performing content selection and curation based on user preferences. The machine learning model autonomously analyzes user behavior patterns and content metadata to generate personalized channel recommendations, eliminating the need for users to manually navigate and evaluate multiple channels.
2Adaptability or versatility
If traditional television channels broadcast content for general audiences, then they can serve diverse viewer groups, but they cannot satisfy individual user preferences and expectations
Solution Approach 1:
The system applies local quality by customizing content delivery for each individual user rather than providing uniform content to all viewers. Machine learning models analyze specific user preferences, viewing habits, and demographic characteristics to generate personalized content recommendations tailored to each user's unique tastes and expectations.
Solution Approach 2:
The system changes parameters by dynamically adjusting content selection based on user profiles, time of day, viewing context, and evolving preferences. The machine learning model continuously refines content recommendations by modifying selection criteria according to observed user behavior patterns and feedback, enabling adaptive personalization.
3Adaptability or versatility
If users manually select and program content for their personalized channel, then they can have full control over content selection, but the complexity and effort of channel creation increases
Solution Approach 1:
The system enables self-service by automatically performing content selection and curation based on user preferences. The machine learning model autonomously analyzes user behavior patterns and content metadata to generate personalized channel recommendations, eliminating the need for users to manually navigate and evaluate multiple channels.
Solution Approach 2:
The machine learning model acts as an intermediary between the vast content library and the user, translating user preferences into specific content recommendations. This intermediary layer handles the complex matching and selection processes, presenting simplified personalized results to users without exposing them to the underlying complexity of content analysis.
Data Source
AI summary
A method for generating a personalized television channel for a user of a terminal configured to access at least one audiovisual content broadcasting service is disclosed. The method includes obtaining user preference criteria in advance; for at least one given time slot, selecting, from the preference criteria obtained, an audiovisual content to be programmed on the personalized television channel, from among a plurality of audiovisual contents accessible from the terminal of the user by the at least one service; and on receipt of a request for access to the personalized television channel from the terminal of the user in the given time slot, transmitting access information to the selected audiovisual content to the terminal of the user, with a view to its playback by the terminal of the user as a program of the personalized channel.