Self-adaptive ML Engine for Dynamic Content Clustering
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Solution Overview
Problem
Current machine learning models lack self-adaptation and personalization capabilities, failing to provide users with dynamically relevant content based on real-time user interactions and diverse features without requiring explicit user preferences or input.
Innovation Solution
A self-adaptive computer system that utilizes a machine learning model, such as Doc2Vec, to transform content data into a N-dimensional vector space, applies clustering techniques to generate user-specific dynamic cluster models, and continuously updates these models based on user actions, biological, social, economic, and behavioral features, to deliver personalized content on portable devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional machine learning models are used to generate content output, then the system can process content data through layered transformations, but the model lacks self-adaptation and personalization capabilities
Solution Approach 1:
The system employs self-training mechanisms where the machine learning model automatically learns from user interactions and feedback without requiring external reconfiguration. The model continuously adapts its parameters based on observed user behavior patterns, enabling self-personalization while maintaining operational simplicity
Solution Approach 2:
The patent implements dynamic cluster models that evolve over time based on user interactions. The clustering structure is not static but adapts its configuration, groupings, and parameters in response to changing user preferences and behaviors, allowing the system to remain versatile without requiring complete system redesign
2Measurement precision
If the system continuously updates user-specific dynamic cluster models based on user actions, then personalization and relevance improve, but computational resources and processing time increase
Solution Approach 1:
The system performs preliminary clustering and model generation during off-peak periods or in advance of user requests. User-specific dynamic cluster models are pre-computed based on available data, and then quickly adjusted in real-time based on actual user interactions, reducing the computational burden during critical content delivery moments
Solution Approach 2:
The system implements incremental updates to cluster models rather than complete re-computations. Only the portions of the model affected by new user interactions are updated, while the rest of the structure remains intact. This partial action approach maintains high personalization accuracy while significantly reducing processing time and resource consumption
3Adaptability or versatility
If the system transforms content data into N-dimensional vector space and applies clustering techniques, then content organization and personalization improve, but computational complexity and resource requirements increase
Solution Approach 1:
The patent divides the content processing into distinct segments: initial bulk transformation to N-dimensional vector space performed once during system setup or data ingestion, followed by lighter-weight clustering operations that operate on the already-transformed data. This segmentation allows the energy-intensive transformation to be amortized over time while keeping real-time personalization computationally efficient
Solution Approach 2:
The N-dimensional vector space transformation is performed in advance during data preprocessing rather than in real-time during content delivery. This preliminary action converts the raw content data into a computationally efficient representation that can be quickly clustered and matched against user profiles without repeating the expensive transformation operation
4Productivity
If the system tracks user actions in real-time to continuously self-adapt models, then user-specific content delivery improves, but system complexity and data processing requirements increase
Solution Approach 1:
The system implements a streamlined feedback loop where user interactions are captured, aggregated into meaningful signals, and fed back to the clustering model for incremental updates. The feedback mechanism is designed to extract only the essential information needed for model adaptation, filtering out redundant data and focusing on signals that genuinely impact personalization quality
Solution Approach 2:
The tracking mechanism is designed to serve multiple functions simultaneously: it monitors user interactions for model adaptation, validates content delivery effectiveness, and maintains user preference profiles. This multi-functionality reduces the need for separate specialized systems, thereby lowering overall complexity while maintaining high content delivery efficiency
Data Source
AI summary
In some embodiments, the present invention provides for a computer system which includes a content database storing initial content data and a vocabulary data set; a processor configured to applying a machine learning model to transform the initial content data into a N-dimensional vector space; self-training the machine learning model based on the vocabulary data set; applying a clustering technique to the N-dimensional vector space to generate a cluster model of clusters, where each cluster includes a plurality of word representations; associating each cluster with a cluster identifier; obtaining subsequent content data; associating each data element of the subsequent content data with each cluster to generate a content data cluster mapping model; continuously tracking, for each user, each respective cluster identifier of each respective cluster associated with each action performed by each user with each data element to continuously self-adapt each user-specific, time-specific dynamic cluster mapping model.