Knowledge-Graph Product Recommendations for Evolving User Preferences
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Solution Overview
Problem
Existing digital recommendation systems struggle to accommodate highly specific and dynamically evolving user interactions, failing to adapt to changing user needs and preferences, and often provide recommendations that are not aligned with personalized constraints, leading to inefficiencies in large-scale data processing and precision.
Innovation Solution
A computer-implemented system using a machine learning model integrated with a decision tree-based algorithm and a dynamically updated knowledge graph, which processes structured and unstructured data to generate personalized recommendations that adapt to evolving user preferences and external factors, incorporating ethical, religious, and environmental criteria, and ensures compliance with regulatory standards through continuous feedback loops.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional recommendation systems use static rule sets or generalized models, then system complexity is reduced and ease of operation is improved, but adaptability to dynamically evolving user preferences and multi-dimensional constraints deteriorates
Solution Approach 1:
The patent implements dynamic adaptability by continuously updating the recommendation model with user feedback and interaction data. The system transitions from static rules to dynamic learning, where recommendations evolve in real-time based on changing user preferences, behaviors, and contextual factors, resolving the contradiction between adaptability and complexity through intelligent automation.
Solution Approach 2:
The system incorporates feedback loops where user interactions, clicks, and preferences are continuously fed back into the recommendation engine. This feedback mechanism enables the system to learn and adapt to evolving user needs dynamically, maintaining high adaptability while managing complexity through automated learning processes rather than manual rule updates.
2Measurement precision
If recommendation systems process vast amounts of structured and unstructured data in real-time, then measurement precision and manufacturing precision of recommendations are improved, but loss of time and productivity deteriorate due to computational demands
Solution Approach 1:
The system performs preliminary data processing and feature extraction during data ingestion and storage phases. By pre-processing and structuring data before it is needed for recommendations, the system reduces real-time computational burden while maintaining high prediction precision, effectively resolving the time-precision trade-off.
Solution Approach 2:
The patent replaces traditional mechanical data processing methods with machine learning-based predictive models. These models learn patterns from historical data and can generate precise recommendations with minimal real-time computation, substituting heavy mechanical processing with intelligent inference that achieves high precision faster.
3Reliability
If the system continuously updates recommendations based on real-time user feedback and external data, then adaptability and reliability are improved, but device complexity and computational resource requirements increase
Solution Approach 1:
The recommendation system performs self-updates by automatically learning from user feedback and external data sources without requiring manual intervention. The model continuously retrains and adapts itself, maintaining high reliability and relevance while managing complexity through automated self-service mechanisms rather than complex manual management systems.
4Adaptability or versatility
If the system incorporates multiple multi-dimensional constraints including ethical, religious, and environmental criteria, then adaptability to diverse user needs and reliability are improved, but device complexity and difficulty of detecting and measuring constraints increase
Solution Approach 1:
The patent segments complex multi-dimensional constraints into distinct categorical dimensions (ethical, religious, environmental, personal preferences). Each constraint type is processed and weighted independently by the machine learning model, allowing the system to accommodate diverse constraints adaptably while managing complexity through modular segmentation rather than monolithic processing.
Data Source
AI summary
An AI-driven recommendation system utilizes a machine learning model and a dynamically updated knowledge graph to generate personalized product recommendations. The system constructs a knowledge graph with nodes and edges representing relationships between users, prior product selections, and historical interactions. A supervised learning framework trains the machine learning model using labeled data from the knowledge graph to predict relevant products based on multi-dimensional constraints. A graphical user interface (GUI) presents dynamically adjusted interactive elements to capture user preferences. User responses are processed using natural language processing (NLP) to refine predictions and generate recommendations. The system continuously updates the knowledge graph with real-time user feedback and external data, retraining the machine learning model to enhance future recommendations. This adaptive approach enables personalized, context-aware recommendations that evolve based on user interactions and external influences.