Machine Learning Substitute Item Recommendation System
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Solution Overview
Problem
Retailers face financial and time losses due to customer dissatisfaction with substitute items chosen by retail associates when original items are out of stock, as current methods rely on personal judgment rather than data-driven approaches.
Innovation Solution
Implementing a system that uses machine learning algorithms to generate matrix data and graphs to identify connection values between items, employing a generative graph convolution network to determine substitute items based on historical data, increasing the likelihood of customer acceptance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If retail associates use personal judgment to select substitute items, then the process is simple and quick, but customer satisfaction decreases and financial losses increase
Solution Approach 1:
The patent introduces a machine learning-based substitute item recommendation system as an intermediary between the retail associate and the final substitute selection. The system processes historical substitution data, item attributes, and customer preferences to generate recommended substitute items, thereby mediating between simple operation and reliable customer acceptance
Solution Approach 2:
The patent replaces the mechanical system of human judgment with an automated machine learning system. The ML model analyzes patterns in historical substitution data and item attributes to objectively determine suitable substitutes, replacing subjective human decision-making with data-driven automation
2Reliability
If a machine learning system is implemented to identify substitute items, then customer acceptance increases, but system complexity increases
Solution Approach 1:
The patent implements preliminary action by pre-training the machine learning model on historical substitution data before deployment. The system pre-processes and stores item attributes, substitution histories, and customer preference patterns in advance, so that when a substitute is needed, the recommendations are generated quickly without complex real-time computation
Solution Approach 2:
The patent utilizes parameter changes by transforming unstructured historical substitution data into structured feature vectors that the machine learning model can process. The system converts item attributes, substitution outcomes, and customer responses into numerical parameters that capture the essence of substitution success without requiring complex qualitative analysis
3Measurement precision
If historical substitution data is analyzed to determine substitute items, then substitution accuracy improves, but data processing time increases
Solution Approach 1:
The patent applies preliminary action by pre-processing historical substitution data during system setup and training phases. The machine learning model is trained in advance on comprehensive historical data, creating a ready-to-use recommendation engine that can quickly query pre-learned patterns rather than analyzing raw historical data in real-time
Solution Approach 2:
The patent implements partial action by analyzing only the most relevant features and attributes when generating substitute recommendations, rather than processing every detail of historical data. The system identifies and focuses on key determining factors for successful substitutions, achieving high accuracy without exhaustive data processing
Data Source
AI summary
This application relates to apparatus and methods for automatically identifying substitute items. A computing device can generate matrix data that identifies connection values between a plurality of items. The matrix data may be generated based on the application of one or more machine learning algorithms to historical data identifying accepted or denied item substitutions. The computing device may then receive item data identifying at least one second item and at least one attribute of that second item. The computing device may generate a graph based on the matrix data and the item data to determine connection values between the second item and the plurality of first items. The computing device may then determine a substitute item (e.g., a replacement item) for the second item based on the connection values between the second item and the plurality of first items.