E-Commerce Product Matching Using Taxonomy-Based Attribute Validation
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Solution Overview
Problem
Existing e-commerce systems face challenges in accurately and efficiently matching products due to complex models requiring high resources and lacking scalability, which affect the accuracy of tasks such as competitive pricing, deduplication, and product grouping.
Innovation Solution
A method and system utilizing a taxonomy tree with deep learning models for product classification, attribute extraction, and search enhancement, including image and text processing, to improve accuracy and scalability in product matching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex models are used to identify duplicate products in a catalog, then measurement precision is improved, but device complexity increases and scalability deteriorates
Solution Approach 1:
The patent segments the product matching process into multiple independent components: (1) product classification into taxonomy categories, (2) attribute extraction and validation, (3) similarity scoring based on validated attributes, and (4) duplicate identification. This segmentation allows each component to be optimized independently, reducing overall system complexity while maintaining high accuracy through specialized processing at each stage.
Solution Approach 2:
The patent changes the approach from using complex black-box models to using structured parameter-based validation. It defines specific product attributes (brand, model, specifications, dimensions) and validates them against taxonomy rules, transforming the problem from pattern recognition to parameter comparison, which is more scalable and interpretable.
2Measurement precision
If complex models are used to identify duplicate products in a catalog, then measurement precision is improved, but productivity deteriorates due to high resource requirements
Solution Approach 1:
The patent performs preliminary product classification into taxonomy categories before conducting duplicate identification. By pre-organizing products into structured categories with validated attributes, the system reduces the search space for duplicate detection and enables faster comparison. This preliminary structuring step significantly improves processing efficiency while maintaining matching accuracy.
Solution Approach 2:
The patent extracts and validates specific product attributes (brand, model, key specifications) from unstructured product data before comparison. By extracting only the relevant validated attributes needed for matching rather than processing entire product datasets, the system reduces computational overhead and improves processing throughput while maintaining accuracy.
3Measurement precision
If complex models are used to identify duplicate products in a catalog, then measurement precision is improved, but adaptability deteriorates
Solution Approach 1:
The patent creates a universal taxonomy framework that can accommodate multiple product categories and types through a common structure of categories, attributes, and validation rules. This universal framework enables the same matching logic to be applied across diverse product types (electronics, clothing, home goods, etc.), improving scalability and adaptability while maintaining consistent accuracy through standardized attribute validation.
Data Source
AI summary
An apparatus for performing product matching may include: a processor configured to receive a search as product item for searching for a catalog product that matches a target product; classify the target product in a product taxonomy tree including a plurality of taxonomy nodes, by identifying a taxonomy node to which the target product belongs, among the taxonomy nodes; obtain product data associated with the target product from an internal source and an external source and measure data quality; extract attributes from the product data based on a machine learning model; validate the attribute in response to the attribute corresponding to defined mandatory attribute of the taxonomy node to which the target product belongs, and provide a search result based on the validated attributes; re-rank searched results governed by relevancy score and display matched set of products to the customer above defined re-rank confidence score.