Intelligent Recommendation System Using Iterative Vector Matrix Generation
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Solution Overview
Problem
E-commerce platforms face challenges in providing personalized product recommendations to customers based on their behavior and purchase data, as existing methods often result in inefficient data analysis and redundant dimensionality, leading to suboptimal recommendation accuracy.
Innovation Solution
The implementation of a system that reduces customer behavior data dimensionality using techniques like Principal Component Analysis (PCA) and iteratively generates customer and product vector matrices, allowing for the computation of product and customer vectors to determine relevant recommendations through distance computations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If customer behavior data is analyzed using traditional methods, then recommendation systems can be built, but data complexity and dimensionality increase leading to reduced recommendation accuracy
Solution Approach 1:
The patent extracts and removes redundant dimensions from customer behavior data through dimensionality reduction techniques. By identifying and eliminating unnecessary features while retaining essential information, the system reduces data complexity without sacrificing recommendation accuracy, directly resolving the contradiction between measurement precision and device complexity
Solution Approach 2:
The patent transforms high-dimensional customer behavior data into lower-dimensional representations while preserving critical patterns. This dimensionality change enables more efficient data processing and analysis, improving recommendation accuracy by focusing on the most relevant features rather than being overwhelmed by redundant dimensions
2Loss of information
If all customer behavior data dimensions are retained, then more information is available for analysis, but redundant dimensionality leads to inefficient data analysis
Solution Approach 1:
The patent extracts only the essential and non-redundant dimensions from customer behavior data. By filtering out duplicate and irrelevant features while preserving meaningful information, the system maintains high information retention rates while significantly improving data analysis efficiency through reduced computational overhead
Solution Approach 2:
The patent transforms the parameter structure of customer behavior data by reducing dimensionality and reorganizing features. This parameter change converts inefficient high-dimensional data into optimized lower-dimensional representations, enabling faster analysis while preserving critical information through intelligent feature selection
Data Source
AI summary
An initial matrix is generated through reducing the dimensionality of initial behavior data defined at an initial space. Purchase data associated with objects is evaluated iteratively to determine mappings associating objects with one or more of the subjects. Based on the determined mappings, an object vector matrix is generated based on average measures over subject vectors included in the initial subject matrix. An updated subject vector matrix including updated subject vectors is generated based on the object vector matrix and the purchase data. Updated object vector matrix versions and subject vector matrix versions are iteratively generated based on latest generated version of the matrixes. According to a validation criteria applied over the iterative generation process, a final object vector matrix and a final subject vector matrix are determined. The final determined matrixes may be analyzed to determine recommendations for objects and subjects.