Item Matching System Using Customer Decision Tree Weights
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current item matching techniques focus on exact matching, which is inefficient due to the high number of attribute combinations, and fail to consider user mindset and qualitative attribute values, making it difficult to identify close matches relevant to customer preferences.
Innovation Solution
A hardware processor-based method and system for item matching that performs attribute enrichment using Machine Learning techniques to quantify qualitative attribute values, standardizes both qualitative and quantitative attributes, and assigns weights based on Demand Transfer values from a Customer Decision Tree to identify partial matching items by calculating a matching score.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If exact matching techniques are used to identify matching items, then matching precision is improved, but the complexity of the system increases and productivity decreases due to the high number of attribute combinations
Solution Approach 1:
The patent segments the item matching process into multiple stages: first performing exact matching on key attributes to identify potential matches, then applying partial matching techniques for remaining attributes. This segmentation reduces the computational complexity by breaking down the overwhelming task of comparing all attribute combinations into manageable steps, thereby improving productivity while maintaining matching precision.
Solution Approach 2:
The patent applies different matching strategies to different attributes based on their importance and characteristics. Critical attributes use exact matching for precision, while less critical attributes use partial matching or fuzzy logic to improve efficiency. This local differentiation of matching quality across attributes resolves the contradiction by optimizing both precision and productivity in their respective domains.
2Measurement precision
If exact matching techniques are used to identify matching items, then matching precision is improved, but device complexity increases due to handling multiple attribute types
Solution Approach 1:
The patent transforms qualitative attributes into quantitative representations that can be processed uniformly by the system. By encoding categorical data (e.g., color, brand) into numerical formats and applying standardized matching algorithms, the system handles multiple attribute types without increasing complexity. This parameter transformation allows exact matching to be applied consistently across diverse attribute types.
Solution Approach 2:
The patent develops a universal matching framework that can handle both exact and partial matching, as well as both qualitative and quantitative attributes, through a single integrated system. This multi-functional approach eliminates the need for separate complex subsystems for different matching scenarios, thereby maintaining matching precision while reducing overall device complexity.
3Productivity
If partial matching is implemented to handle qualitative attribute values, then productivity is improved, but matching precision deteriorates due to inability to perform direct comparison
Solution Approach 1:
The patent introduces an intermediary encoding layer that translates qualitative attribute values into a standardized format suitable for computational comparison. This intermediary representation allows partial matching algorithms to operate on qualitative data with the same precision as quantitative data, resolving the contradiction by enabling direct comparison through transformation rather than approximation.
4Adaptability or versatility
If user mindset and qualitative attribute values are considered in item matching, then adaptability is improved, but device complexity increases due to additional processing requirements
Solution Approach 1:
The patent performs preliminary encoding and standardization of qualitative attributes and user preference data before the actual matching process. By pre-processing this information into structured formats, the system can adapt to user mindsets and qualitative variations without adding complexity during the core matching operation. This preliminary preparation enables adaptability while maintaining processing efficiency.
Data Source
AI summary
Existing approaches for item matching that are used for retail strategies are based on similarity matching, however, do not consider user mindset, magnitude present across quantitative AVs and segment specific customer interest on certain qualitative AVs. Embodiments of the present disclosure provide a method and system for Machine Learning (ML) based item matching by considering user mindset, magnitude present across quantitative AVs and segment specific customer interest on certain qualitative AV. The item matching approach disclosed, performs data analytics at the AV level to identify possible close matching items from the list of available partially matching as well as non-matching items. The method disclosed primarily performs Attribute (AT) enrichment by quantizing all the qualitative AVs to be analyzed. Weights are assigned to all the quantized AVs based on a Demand Transfer (DT) value provided by a Customer Decision Tree (CDT), wherein the CDT captures the user mindset.