ML-Based Content Insertion Scheduling for Channel Utilization
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Solution Overview
Problem
Existing content delivery systems face challenges in maximizing the utilization of content stream channels without causing collisions, as these channels are often not fully utilized, leading to inefficiencies in content insertion.
Innovation Solution
The implementation of a system that uses machine learning-based prediction models to automatically identify unutilized spots in content stream channels and generate content insertion schedules to maximize channel utilization while minimizing potential collisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If content is inserted into content stream channels at specified times, then content delivery is achieved, but channel utilization is not maximized and collisions may occur
Solution Approach 1:
The system performs preliminary actions by predicting unutilized spots in content stream channels before actual content insertion. Machine learning models analyze historical data to forecast optimal insertion times and positions, allowing the system to proactively schedule content without causing collisions and maximizing channel utilization efficiency
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring content stream channel utilization and using this information to train and refine machine learning prediction models. This feedback loop enables the system to learn from actual performance data and improve its predictions for optimal content insertion scheduling
2Productivity
If machine learning models are used to predict unutilized spots, then content insertion opportunities increase, but system complexity increases
Solution Approach 1:
The system introduces machine learning prediction models as intermediary components that bridge the gap between content inventory and channel scheduling. These models process historical utilization data and output predicted unutilized spots, simplifying the overall system architecture by using predictive algorithms rather than complex real-time optimization algorithms
Solution Approach 2:
The system performs preliminary data processing and model training using historical data, creating prediction models that can be applied to future scheduling decisions. This preliminary action reduces the computational complexity during actual content insertion by using pre-trained models rather than real-time complex calculations
Data Source
AI summary
The present teaching relates to method, system, medium, and implementations for increasing content insertion opportunities. A prediction input is received that characterizes utilization of content stream utilization channels (CSUCs). Schedule parameters are automatically predicted for the prediction input using prediction models obtained via machine learning based on grouped historic data related to CSUCs, where grouping is based on an operational mode in which the prediction models operate. Using the predicted schedule parameters, insertion opportunity may be identified with respect to CSUCs and insertion schedules are generated specifying insertions of content streams into identified CSUCs at respective insertion times.