CONVERTCART SOLUTIONS PRIVATE LIMITED (20240242257). SYSTEM AND METHOD FOR EXTRACTING HIERARCHICAL INFORMATION FROM A DATASET CONTAINING ONE-TO-MANY MAPPINGS simplified abstract
Contents
- 1 SYSTEM AND METHOD FOR EXTRACTING HIERARCHICAL INFORMATION FROM A DATASET CONTAINING ONE-TO-MANY MAPPINGS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 SYSTEM AND METHOD FOR EXTRACTING HIERARCHICAL INFORMATION FROM A DATASET CONTAINING ONE-TO-MANY MAPPINGS - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about Product Categorization
- 1.13 Original Abstract Submitted
SYSTEM AND METHOD FOR EXTRACTING HIERARCHICAL INFORMATION FROM A DATASET CONTAINING ONE-TO-MANY MAPPINGS
Organization Name
CONVERTCART SOLUTIONS PRIVATE LIMITED
Inventor(s)
KANIJ FATEMA Aleya of WEST BENGAL (IN)
SYSTEM AND METHOD FOR EXTRACTING HIERARCHICAL INFORMATION FROM A DATASET CONTAINING ONE-TO-MANY MAPPINGS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240242257 titled 'SYSTEM AND METHOD FOR EXTRACTING HIERARCHICAL INFORMATION FROM A DATASET CONTAINING ONE-TO-MANY MAPPINGS
Simplified Explanation
The proposed solution aims to create a user-friendly hierarchical structure for categorizing products on a website, improving data quality and performance of features like recommendations and search.
- Mapping of category product
- Sorting categories
- Getting immediate superset category
- Comparing category with a specified threshold value
- Mapping the immediate superset to immediate subset category
- Generating subset category
- Mapping category-category
- Generating category hierarchy
- Mapping product-category
- Generating product hierarchy
- Getting level-wise categories
Key Features and Innovation
- Efficient mechanism for creating a user-friendly hierarchical structure for product categorization - Improves data quality and performance of features like recommendations and search - Determines optimum categories for generating product recommendations
Potential Applications
This technology can be applied to e-commerce and non-e-commerce websites to enhance product categorization and improve user experience.
Problems Solved
- Simplifies the process of categorizing products on a website - Enhances data quality and performance of features like recommendations and search - Helps in generating more accurate product recommendations
Benefits
- Improved user experience on websites - Enhanced data quality and performance of features - Optimum categorization for better product recommendations
Commercial Applications
Optimizing product categorization for e-commerce websites to improve user experience and increase sales.
Prior Art
Further research can be conducted in the field of product categorization and hierarchical structures in e-commerce and non-e-commerce websites.
Frequently Updated Research
Stay updated on advancements in product categorization algorithms and techniques for e-commerce websites.
Questions about Product Categorization
1. How does this technology impact the user experience on e-commerce websites?
- This technology enhances user experience by simplifying product categorization and improving the accuracy of product recommendations.
2. What are the potential challenges in implementing this hierarchical structure for product categorization?
- Challenges may include mapping complex product categories, setting threshold values, and ensuring accurate categorization for diverse product ranges.
Original Abstract Submitted
the proposed solution provides an efficient mechanism to create a user-friendly hierarchical structure category of products for an e-commerce or non-e-commerce website. the method includes the step of mapping of category product, sorting categories, getting immediate superset category, comparing category with a specified threshold value, mapping the immediate superset to immediate subset category, generating subset category, mapping category-category, generating category hierarchy, mapping product-category, generating product hierarchy, and at last getting level-wise categories. this category hierarchical information for each product improves the data quality and performance of features like recommendations, search, and other product discovery solutions and determines the optimum categories to consider while generating product recommendations.
