AI-Based Recommendation Filtering for Negative User Sentiment
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Solution Overview
Problem
Existing recommendation systems fail to effectively detect and filter out offensive content due to outdated manual lists, reliance on keywords, and inability to adapt to changing user sentiments, leading to negative user experiences.
Innovation Solution
The implementation of artificial intelligence and machine learning techniques to build computer models that identify negative user sentiment cues, predict offensive recommendations, and filter out items likely to offend users, using keyword-based and event analysis models to modify recommendations in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual lists and keyword-based filtering are used to detect offensive content, then the system is simple to implement, but the detection precision and adaptability to changing user sentiments deteriorate
Solution Approach 1:
The patent replaces manual keyword-based filtering (mechanical system) with machine learning models that automatically learn offensive content patterns from user feedback data. The system uses supervised learning algorithms to train classifiers that detect offensive recommendations, substituting static keyword matching with dynamic, adaptive computational models that improve detection precision while maintaining implementation feasibility through automated training pipelines.
Solution Approach 2:
The system dynamically changes detection parameters by continuously retraining machine learning models with new user feedback data. The offensive content detection thresholds, feature weights, and model parameters are adjusted based on evolving user sentiments and feedback patterns, allowing the system to adapt to changing offensive content characteristics without manual intervention.
2Device complexity
If manual lists and keyword-based filtering are used, then the device complexity is low, but the adaptability to changing user sentiments deteriorates
Solution Approach 1:
The system implements self-service adaptability through automated machine learning pipelines that continuously collect user feedback, retrain detection models, and update filtering parameters without manual intervention. The system serves itself by automatically adapting to changing user sentiments through feedback-driven model retraining, eliminating the need for manual list updates while maintaining low operational complexity.
Solution Approach 2:
The patent incorporates feedback loops where user responses to recommendations (positive or negative) are continuously collected and used to retrain machine learning models. This feedback mechanism enables the system to adapt to changing user sentiments by learning from actual user interactions, allowing the detection system to evolve with user preferences while maintaining manageable system complexity through automated processes.
3Adaptability or versatility
If AI and machine learning techniques are implemented to dynamically filter recommendations, then the adaptability to user sentiments improves, but the device complexity increases
Solution Approach 1:
The patent segments the recommendation system into distinct functional modules: user feedback collection, feature extraction, model training, and recommendation filtering. Each module performs a specific function, allowing the complex AI/ML system to be managed through modular architecture. This segmentation reduces overall system complexity by enabling independent development, testing, and maintenance of each component while maintaining high adaptability through coordinated operation of specialized modules.
4Measurement precision
If AI models analyze user feedback and behavior data to predict offensive content, then the detection precision improves, but the loss of time for data processing increases
Solution Approach 1:
The system performs preliminary actions by pre-processing user feedback data and pre-training machine learning models offline before deployment. Feature extraction, data cleaning, and model training are conducted in advance, allowing the actual recommendation filtering to use pre-computed features and trained models. This preliminary processing reduces real-time data processing time while maintaining high detection precision through sophisticated pre-analyzed models.
Data Source
AI summary
A recommendation system uses artificial intelligence to identify, based on negative sentiment cues from users, item attributes, such as keywords, that users may find offensive or undesirable. The negative sentiment cues may be explicit (e.g., a user selects an option not to view a particular recommendation again), implicit (e.g., a user does not interact with recommendations relating to an attribute), or both. The system may use a computer model generated based on these identified attributes to filter or modify recommendations to a user or group of users. For instance, if a particular keyword is identified as highly offensive to a group of users, items associated with the keyword may be filtered from item recommendations presented to the group of users. If an attribute is identified as moderately offensive to a user, items associated with the attribute may be down-weighted in item recommendations presented to the user.