Proactive Supplemental Content Filtering with ML Topic and Frustration Signals
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing natural language processing systems struggle to effectively filter and manage supplemental content output to users, often providing unbeneficial content that can lead to user frustration.
Innovation Solution
A system that utilizes a filtering component with machine learning techniques to determine content topics and user frustration data, ensuring that only relevant and user-friendly content is proactively output, while minimizing undesirable content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the system proactively outputs supplemental content to users, then user engagement and information delivery are improved, but user frustration increases due to irrelevant or unbeneficial content
Solution Approach 1:
The system implements a feedback mechanism where user responses to supplemental content are captured and used to train machine learning models. This feedback loop enables the system to learn from user behavior patterns and refine its content selection algorithms, thereby reducing user frustration while maintaining proactive content delivery effectiveness.
Solution Approach 2:
The system dynamically adjusts content delivery parameters based on user preferences and context. By changing parameters such as content timing, relevance thresholds, and delivery frequency based on learned user patterns, the system optimizes the balance between proactive information delivery and user satisfaction.
2Quantity of substance
If the system outputs more supplemental content, then information completeness is improved, but the quality and relevance of content decreases
Solution Approach 1:
The system employs machine learning models that dynamically adjust content relevance parameters based on user profile, context, and historical data. This allows the system to maintain high content relevance even as the volume of supplemental content increases, by intelligently filtering and prioritizing the most useful information.
Solution Approach 2:
The system replaces simple mechanical content filtering with sophisticated machine learning-based relevance assessment. This substitution enables the system to handle large volumes of content while maintaining high relevance through intelligent pattern recognition and prediction algorithms.
3Measurement precision
If the system uses machine learning to filter content, then content quality is improved, but system complexity increases
Solution Approach 1:
The system introduces machine learning models as intermediary components between content generation and delivery. These models act as mediators that process raw content through relevance filtering and selection, managing system complexity by encapsulating complex filtering logic in dedicated ML modules rather than distributing it throughout the entire system.
Data Source
AI summary
Techniques for filtering the output of supplemental content are described. When a supplemental output system (e.g., a supplemental content system or notification system) receives supplemental content for output, the supplemental output system sends a user identifier (of the recipient user) and the supplemental content to separately implemented filtering component. The filtering component uses a machine learning (ML) model to determine a topic of the supplemental content. The filtering component determines whether the supplemental content should not be output based on the ML model-determined topic, one or more guardrail policies of the supplemental output system, and user frustration data regarding previously output supplemental content. Use of the ML model to determine the topic prevents a content publisher from surreptitiously associating supplemental content with a specific topic in an effort to bypass topic-based output guardrails.