Machine Learning Model for Inappropriate Content Filtering
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Solution Overview
Problem
Conventional approaches are ineffective in identifying and removing inappropriate material, such as offensive content, from social networking systems as the volume of user-generated content grows, leading to negative user experiences and increased challenges in filtering out variations of offensive material.
Innovation Solution
A machine learning model is trained using a large dataset of labeled positive and negative data, with supplemental training data generated through reformulation, co-click analysis, and embeddings to identify inappropriate material, and applied to filter out offensive content by determining confidence values exceeding a threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional filtering approaches are used to identify inappropriate material, then the filtering process is simple to implement, but the effectiveness in identifying and removing inappropriate material decreases as the volume of user-generated content grows
Solution Approach 1:
The patent introduces an embedding model as an intermediary component between the training data and the machine learning classifier. This embedding model transforms training data into embeddings that capture semantic relationships, enabling the classifier to more effectively identify inappropriate material while maintaining system manageability through modular architecture.
Solution Approach 2:
The patent applies preliminary action by generating supplemental positive training data through reformulation and co-click analysis before the actual classification task. This preprocessing step enhances the quality and quantity of training data, improving the classifier's effectiveness in identifying inappropriate material while keeping the core classification algorithm relatively simple.
2Measurement precision
If a machine learning model is trained with large datasets and supplemental data generation to improve identification accuracy, then the ability to identify inappropriate material improves, but the training time and computational resources increase
Solution Approach 1:
The patent uses copying by generating supplemental positive training data through reformulation of existing training data. Instead of manually creating diverse training examples, the system automatically generates variations and related instances, reducing the time and resources needed to assemble large datasets while maintaining high identification accuracy.
Solution Approach 2:
The patent replaces manual data curation and labeling processes with automated machine learning pipelines. The embedding model and supplemental data generation algorithms substitute for time-consuming manual efforts in collecting and preparing training data, significantly reducing training time while improving accuracy through systematic data synthesis.
3Adaptability or versatility
If traditional filtering methods are used, then the system is easy to operate, but it cannot effectively handle variations of offensive material
Solution Approach 1:
The patent implements universality by designing a machine learning classifier that can handle multiple types of inappropriate material (offensive, profane, racist, etc.) and their variations through a single unified model. The embedding-based approach enables the system to generalize across different forms and expressions of inappropriate content, providing versatile filtering capability while maintaining a relatively simple operational interface.
Data Source
AI summary
Systems, methods, and non-transitory computer-readable media can be configured to acquire at least one instance of positive training data based at least in part on at least one source. A set of supplemental positive training data can be generated based at least in part on the at least one instance of positive training data. A machine learning model can be trained to identify inappropriate material based at least in part on the set of supplemental positive training data.