Machine Learning Content Targeting System
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Solution Overview
Problem
Existing content generation systems often send untargeted content to users, leading to resource wastage and low user engagement due to the lack of personalized and relevant content, as they rely on time or event triggers rather than user-specific interests and behaviors.
Innovation Solution
A machine learning-based system that calculates the probability of user engagement by analyzing historical data and similar user profiles to generate and send content only when there is a high likelihood of interest, using a three-tiered architecture with modules for communication, data management, calculation, and content generation to personalize content delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If content is generated and sent based on time or event triggers, then content delivery frequency is maintained, but resource wastage increases and user engagement decreases
Solution Approach 1:
The system changes the parameter of content delivery from fixed time/event intervals to dynamic probability-based triggering. Machine learning models calculate engagement probability scores that dynamically determine whether to send content, transforming the delivery mechanism from periodic to selective based on predicted user interest.
Solution Approach 2:
The system enables content delivery decisions to be self-determined by analyzing user behavior patterns and engagement history. The machine learning models automatically assess whether content should be sent based on learned user preferences, eliminating the need for manual scheduling and reducing unnecessary resource consumption.
2Productivity
If content is generated and sent at specific time intervals, then systematic content delivery is achieved, but user engagement decreases due to low relevance
Solution Approach 1:
The system implements feedback loops where user interactions with content (clicks, ignores, engagements) are continuously fed back into machine learning models. These models adjust engagement probability predictions based on observed user behavior, creating a closed-loop system that improves content relevance over time while maintaining systematic delivery.
Solution Approach 2:
The content delivery system transitions from static time-based scheduling to dynamic probability-based triggering. The engagement probability scores dynamically adjust based on real-time user behavior analysis, allowing the system to adapt content delivery timing and selection to individual user preferences while maintaining systematic operation.
3Productivity
If content is generated based on time triggers, then consistent content updates are provided, but resource consumption increases for generating low-interest content
Solution Approach 1:
The system applies partial action by selectively generating and sending content only when engagement probability exceeds certain thresholds. Instead of generating content at every time interval, the machine learning models determine which intervals warrant content generation based on predicted user interest, reducing overall resource consumption while maintaining consistency for high-value opportunities.
4Device complexity
If traditional content targeting is used, then simple delivery mechanisms are maintained, but personalization and relevance are lost
Solution Approach 1:
The system introduces machine learning models as intermediary components between the content generation system and users. These models act as intelligent mediators that analyze user profiles, behavior patterns, and content characteristics to determine optimal delivery decisions, adding personalization capability while maintaining the underlying simple content generation and delivery infrastructure.
Data Source
AI summary
A system and method for content generation and targeting using machine learning are provided. In example embodiments, a probability that a user will visit a webpage based on historical data is calculated. A probability that the user will engage with a particular content category based on past user engagement is calculated. In response to the probability of the user engaging with the particular content category being equal to or greater than a first threshold, the content is generated. Further, in response to the probability of the user not visiting a webpage meeting or exceeding a second threshold, the generated content is sent to the user.