Probability-Based Content Modification Device Selection
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Solution Overview
Problem
Existing content-modification systems face inefficiencies and computational expenses when attempting to ensure a large number of content-presentation devices successfully perform dynamic ad replacement (DAI) operations due to varying probabilities of success based on factors like bandwidth and transmission delays.
Innovation Solution
The system identifies a group of content-presentation devices tuned to a channel with an upcoming content modification opportunity, determines the probability of each device successfully performing a content-modification operation, and selects a subgroup based on these probabilities to efficiently facilitate the desired operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the system attempts to ensure a large number of content-presentation devices successfully perform DAI operations, then the reliability of content modification increases, but the computational expenses and system complexity increase significantly
Solution Approach 1:
The system changes the parameter of device selection from random or uniform selection to probability-based selection, where each device's selection probability is determined by its historical success rate. This parameter change allows the system to prioritize devices with higher reliability without requiring complex coordination mechanisms, thus improving DAI success rates while maintaining manageable system complexity.
2Productivity
If the system selects more content-presentation devices to perform DAI operations, then the productivity of content modification increases, but the computational expenses increase
Solution Approach 1:
The system applies partial action by selecting only a subset of devices for DAI operations based on probability thresholds rather than attempting to coordinate all available devices. This approach achieves sufficient productivity by leveraging the contributions of high-probability devices without incurring the computational costs of managing and coordinating a large number of devices, thus optimizing the balance between productivity and computational expense.
3Productivity
If the system uses probability-based selection to choose content-presentation devices, then the efficiency of device selection improves, but the measurement precision requirements increase
Solution Approach 1:
The system uses historical operation data as a proxy or copy for predicting future success probabilities, rather than requiring real-time, high-precision measurements of each device's current state. This copying approach allows the system to make efficient selection decisions based on past performance patterns, reducing the need for complex real-time measurement and analysis while maintaining effective device selection.
Data Source
AI summary
In one aspect, a method includes identifying an upcoming content modification opportunity on a channel. The method also includes responsive to identifying the upcoming content modification opportunity on the channel, identifying a group of multiple content-presentation devices tuned to the channel. The method also includes for each content-presentation device in the identified group, determining a respective probability of that content-presentation device successfully performing a respective content-modification operation in connection with the identified upcoming content modification opportunity on the channel. The method also includes using at least the determined probabilities as a basis to select, from among the content-presentation devices in the identified group, a subgroup of content-presentation devices. The method also includes performing actions that facilitate causing each of at least some of the content-presentation devices in the selected subgroup to perform a respective content-modification operation in connection with the identified upcoming content modification opportunity on the channel.