ML Sensitive Content Identification for Marketing
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Solution Overview
Problem
Marketers face challenges in controlling the personalization of online content, leading to undesired advertisements that reduce consumer satisfaction and impact revenue, as existing methods are time-consuming and inaccurate in identifying sensitive content.
Innovation Solution
A machine learning model is used to analyze marketing content and predict sensitive subject matter, trained with an initial set of sensitive topics expanded using natural language processing, to identify and adapt content, reducing unnecessary resource utilization and improving consumer satisfaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to identify sensitive content, then accuracy is improved, but time consumption increases
Solution Approach 1:
The machine learning model is trained in advance with sensitive topic data and audience segment information before content deployment. This preliminary training enables the model to quickly identify sensitive content without requiring time-consuming manual review processes during actual content delivery, thus resolving the contradiction between accuracy and time consumption.
Solution Approach 2:
The patent replaces manual content review mechanisms with an automated machine learning-based sensitive content identification system. This substitution eliminates the need for human reviewers to manually examine each piece of content, dramatically reducing time consumption while maintaining or improving identification accuracy through the model's learning capabilities.
2Productivity
If personalized content is delivered to online consumers, then conversion rates are improved, but consumer satisfaction deteriorates due to undesired advertisements
Solution Approach 1:
The system monitors audience segment movement and behavior in real-time, using this feedback to identify when personalized content becomes sensitive or undesired. The machine learning model adjusts content delivery based on this feedback, preventing consumer frustration while maintaining effective personalization for conversions.
Solution Approach 2:
The sensitive content identification system dynamically adapts to changing audience preferences and sensitivities. By continuously monitoring audience segment movement and updating the machine learning model, the system can adjust personalized content delivery in real-time, ensuring that conversions are maintained while avoiding content that may cause consumer frustration.
3Measurement precision
If manual content review is performed to identify sensitive content, then accuracy is improved, but device complexity increases
Solution Approach 1:
The machine learning model serves multiple functions: it identifies sensitive content, monitors audience segment movement, and provides recommendations for content adjustment. This multi-functionality eliminates the need for separate manual review processes and additional complex systems, reducing overall device complexity while maintaining identification accuracy.
Data Source
AI summary
Methods and systems are provided for facilitating identification of sensitive content. In embodiments described herein, a set of sensitive topics is obtained. Each sensitive topic in the set of sensitive topics can include subject matter that may be deemed sensitive to one or more individuals. Thereafter, the set of sensitive topics is expanded to an expanded set of sensitive topics using a first machine learning model. The expanded set of sensitive topics is used to train a second machine learning model to predict potential sensitive content in relation to input content.