Targeted Media Playback Personalization for Ad Load Control
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Solution Overview
Problem
Existing media streaming systems fail to accommodate varying user preferences for the amount of targeted media content, leading to inconsistent viewing experiences.
Innovation Solution
A system and method that allows users to input their preferred level of exposure to targeted media content, using machine learning algorithms to customize the amount, frequency, and type of targeted media content based on user inputs, and adjust playback settings accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If targeted media content is added to media content items, then advertising revenue and content customization options are improved, but user satisfaction deteriorates when the amount of targeted content exceeds user preferences
Solution Approach 1:
The system dynamically adjusts the amount and type of targeted media content inserted into content items based on real-time analysis of user feedback, preferences, and consumption patterns. This dynamic customization allows the system to adapt content delivery to individual user tolerances, resolving the contradiction between providing customized content and maintaining user satisfaction.
Solution Approach 2:
The system implements a feedback loop where user interactions with targeted content (such as skipping, engaging, or expressing preference) are continuously monitored and used to adjust future content insertion decisions. This feedback mechanism ensures that targeted content delivery remains aligned with user preferences, preventing oversaturation while maintaining customization benefits.
2Productivity
If the amount of targeted media content is increased to maximize advertising opportunities, then productivity is improved, but user satisfaction deteriorates due to excessive targeted content
Solution Approach 1:
The system changes key parameters such as the quantity, frequency, and timing of targeted content insertion based on user profiles and real-time feedback. By dynamically adjusting these parameters rather than using fixed high-volume insertion, the system maximizes advertising delivery within user tolerance limits, resolving the contradiction between productivity and user satisfaction.
3Object-affected harmful factors
If playback settings are customized for each user, then user satisfaction is improved, but device complexity increases
Solution Approach 1:
The system automatically generates and applies customized playback settings by analyzing user behavior patterns and preferences without requiring manual user configuration. This self-service approach to customization maintains high user satisfaction while avoiding the complexity of manual setup interfaces and user education requirements.
Solution Approach 2:
The system implements a universal playback settings engine that serves multiple functions: analyzing user preferences, generating customized settings, inserting targeted content, and adjusting delivery parameters. This multi-functional approach consolidates complexity into a single system rather than requiring separate mechanisms for each customization aspect.
Data Source
AI summary
Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for processing, understanding, and defining media content. An example process can include receiving, from a media device, a user input indicative of a preferred level of exposure to targeted media content; configuring, based on the user input, one or more playback settings associated with a media content item to accommodate a customized amount of the targeted media content during playback of the media content item; and sending, to the media device, the media content item and the customized amount of the targeted media content.