Hyper-Graph Retail Data Processing for Context-Aware Recommendations
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Solution Overview
Problem
Existing commercial systems for predictive analytics are limited in their ability to extract and understand complex behavioral and contextual patterns on an individual basis and at various groupings of customers and products, particularly in applications such as recommendations, personalization, and content understanding.
Innovation Solution
A method and system that utilizes a hyper-graph structure to dynamically connect and interpret data at various levels of granularity, enabling the generation of recommendations and predictions through artificial intelligence and reasoning techniques, allowing for a deep understanding of customer and product data, and facilitating comparisons across different entities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional data mining and profile classifications are used, then the system can process data efficiently, but it cannot extract and understand complex behavioral and contextual patterns on an individual basis
Solution Approach 1:
The patent segments complex customer behavior data into discrete events and interactions, organizing them into structured formats that can be processed individually while maintaining contextual relationships. This segmentation enables the system to analyze complex patterns without being overwhelmed by data volume or complexity.
Solution Approach 2:
The patent introduces a new dimensional framework for organizing customer data, moving beyond traditional flat databases to a multi-dimensional structure that captures behavioral, contextual, and temporal relationships. This dimensional transformation enables sophisticated pattern recognition while maintaining computational efficiency.
2Measurement precision
If complex analysis techniques are applied to understand individual customer behavior, then recommendation accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary organization and structuring of customer data in advance, creating ready-to-analyze event sequences and behavioral patterns. This preliminary action reduces the computational burden during real-time recommendation generation, enabling fast processing without sacrificing accuracy.
Solution Approach 2:
The patent applies different analysis techniques to different segments of customer data based on their specific characteristics and relevance. By focusing computational resources on the most informative local patterns rather than uniformly processing all data, the system achieves high accuracy with reduced processing time.
3Adaptability or versatility
If traditional ontologies and profile classifications are used, then the system structure remains simple, but it cannot provide deep contextual understanding for personalization
Solution Approach 1:
The patent implements dynamic data structures that adapt to individual customer behaviors and contextual patterns. Rather than using static ontologies, the system dynamically organizes and reorganizes data based on observed patterns, enabling deep personalization while managing complexity through adaptive structuring.
Solution Approach 2:
The patent creates a universal data framework that can handle multiple types of customer interactions and behavioral patterns within a single structure. This multi-functional approach enables the system to provide deep contextual understanding across diverse scenarios without requiring separate complex structures for each case.
Data Source
AI summary
A method and system for emergent data processing are described. A system having one or more servers operable to handle retail data can receive content including customer data and product data. The content can be normalized and stored into a hyper-graph structure in the servers. The system can be used to select a portion of the hyper-graph structure based on a particular customer and to generate a recommendation for the particular customer based on the content in that portion of the hyper-graph structure. The system can also generate personal catalogs based on the information in the hyper-graph structure. The system can perform competitive analysis between products from different sources and include the results in the recommendations. Moreover, the system can perform a vertical analysis of consumable products to provide recommendations for tools or products that can be used in connection with the consumable products.