Retail Product Allocation via Knowledge Graphs and Community Detection
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Solution Overview
Problem
Existing retail product management systems fail to provide personalized and relevant information to different user personas, leading to inefficient decision-making due to varying considerations and reaction times among merchants, sales managers, and market researchers.
Innovation Solution
Implementing machine learning-based systems that utilize knowledge graphs to identify and update associations between entity nodes, applying community detection models to personalize anomaly notification information for specific user personas, and continuously refine linkages based on feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If generic product management information is provided to all users, then system complexity is reduced, but information relevance and decision-making effectiveness deteriorate
Solution Approach 1:
The patent segments users into different personas (merchants, sales managers, market researchers) with distinct information needs, and segments the information delivery system to provide customized content to each persona. This segmentation enables tailored information relevance without requiring complete system redesign, as each persona receives only their specific relevant data.
Solution Approach 2:
The system applies local quality by providing different types of information to different user personas based on their specific roles and needs. Merchants receive operational data, sales managers receive performance metrics, and market researchers receive trend analysis. This localized information quality approach maintains system simplicity while enhancing adaptability to individual user requirements.
2Measurement precision
If detailed product performance tracking is implemented, then measurement precision is improved, but computational overhead and memory storage increase
Solution Approach 1:
The patent extracts and isolates only the specific performance metrics relevant to each user persona from the complete product data set. Instead of processing all available data for every user, the system extracts only the necessary subset (e.g., sales volume for merchants, profit margins for sales managers), thereby maintaining measurement precision while reducing computational overhead and memory storage requirements.
Data Source
AI summary
Some embodiments provide systems to control customized retail product performance information, comprising: a linkage mapping system to define and update linkings within a knowledge graph; a personalization recommendation system controlling different display systems to control graphical user interfaces presenting customized anomaly notification information specific to intended recipients as a function of the linkings; and a community detection system applying a set of machine learning community detection models to identify additional relationships between two or more of the entity nodes, based on feedback data from multiple intended recipients, and cause the linkage mapping system to update the multi-level linkages to embed one or more additional association links between the two or more of the entity nodes; wherein the personalization recommendation system is configured to control, based on the updated additional association links, a first graphical user interface to present first customized anomaly notification information specific to a first intended recipient.