Knowledge-Graph Contextualization for Machine-Learning Interest Identification
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Solution Overview
Problem
Conventional analytic algorithms struggle to accurately identify user-specific interests from electronic data due to the large volume of information, often including irrelevant insights and overlooking rich contextual information, leading to inefficient and inaccurate recommendations.
Innovation Solution
A machine-learning-based approach utilizing a contextualization mechanism with knowledge graphs to interpret user activity data, constructing context data structures, and leveraging a trained machine learning model to generate user-specific content recommendations aligned with predefined user interests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional analytic algorithms process all electronic data to identify user interests, then comprehensive coverage is achieved, but processing time and computational resources increase significantly
Solution Approach 1:
The patent segments the electronic data into different types (work emails, personal emails, spam emails, etc.) and processes each segment separately with appropriate algorithms. This segmentation allows the system to focus computational resources on relevant data while filtering out irrelevant portions, thereby reducing overall processing time while maintaining comprehensive interest identification coverage.
Solution Approach 2:
The patent applies different processing qualities to different data segments based on their relevance. High-quality detailed analysis is applied to work-related emails that are more likely to contain professional interests, while simplified processing is applied to spam and personal emails. This local quality approach optimizes the balance between identification accuracy and processing efficiency.
2Measurement precision
If conventional analytic algorithms process all electronic data without context, then all data is utilized, but irrelevant information increases noise and reduces identification accuracy
Solution Approach 1:
The patent extracts and removes irrelevant information from the electronic data before processing. By identifying and filtering out spam emails, personal emails, and other non-relevant content, the system extracts only the meaningful signal related to user interests. This extraction process significantly improves the signal-to-noise ratio and enhances identification accuracy.
Solution Approach 2:
The patent performs preliminary filtering and categorization of electronic data before the main interest identification process. By pre-processing the data to remove obviously irrelevant content and organize remaining data by type and relevance, the system prepares a cleaner input set for the analysis algorithms, thereby improving accuracy while reducing the computational burden during the main processing phase.
3Use of energy by moving object
If simplistic analytic algorithms are used to process electronic data, then computational resources are conserved, but rich contextual information is overlooked
Solution Approach 1:
The patent segments the analysis process into multiple stages with increasing complexity. Initial filtering uses simple rules to eliminate irrelevant data, then progressively more sophisticated algorithms are applied to increasingly refined data subsets. This segmented approach allows rich contextual analysis to be applied only where needed, conserving computational resources while still extracting meaningful information.
Solution Approach 2:
The patent applies sophisticated contextual analysis selectively to specific data segments rather than uniformly to all data. By concentrating advanced algorithmic processing on work-related emails and other high-value segments while using simpler processing for lower-value segments, the system achieves partial excessive action - applying more computation than strictly necessary only where it provides the greatest return in information extraction efficiency.
Data Source
AI summary
A method for providing user-specific content recommendations to a user may comprise selecting a user interest from a plurality of predefined user interests, extracting user activity data associated with the selected user interest, constructing a context data structure associated with the selected user interest based on a predefined knowledge graph data structure associated with the plurality of predefined user interests, generating one or more new user interests by providing the constructed context data structure and the user activity data to a trained machine learning model, generating a user-specific content recommendation based on the one or more new user interests, and providing the user-specific content recommendation to the user.