Webpage Grouping Interface with Automated Mapping and User Validation
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Solution Overview
Problem
Current web analysis solutions lack the ability to efficiently and accurately map webpages to page groups, limiting user interaction analysis and requiring manual configuration, which is time-consuming and prone to errors.
Innovation Solution
The experience analytics system provides user interfaces for automatically mapping webpages to page groups by determining prioritized terms and conditions, allowing users to edit and confirm these mappings, thereby creating meaningful page groups and improving mapping accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual configuration is used for mapping webpages to page groups, then mapping accuracy can be controlled, but it is time-consuming and requires significant user effort
Solution Approach 1:
The system performs preliminary automatic mapping of webpages to page groups using machine learning models before user review. This pre-computation reduces the configuration time by handling the initial mapping task automatically, while users only need to review and adjust the pre-generated results to ensure accuracy.
2Productivity
If automatic mapping is implemented, then configuration time is reduced, but mapping accuracy may deteriorate
Solution Approach 1:
The system implements a feedback mechanism where users can review automatic mapping results, provide corrections, and adjust page group assignments. This feedback loop allows the system to learn from user corrections and improve future automatic mappings, ensuring both high productivity and maintained accuracy.
Solution Approach 2:
The system provides self-service capabilities where users can independently review, validate, and adjust automatic mapping results without requiring manual configuration from scratch. This allows users to quickly correct any inaccuracies while maintaining overall configuration efficiency.
3Reliability
If comprehensive manual mapping is performed, then mapping thoroughness is improved, but computational resources are wasted on tasks that could be automated
Solution Approach 1:
The mapping task is segmented into two parts: automatic initial mapping performed by machine learning models for routine webpage classification, and selective user review for complex or uncertain cases. This segmentation reduces computational resource waste by automating suitable tasks while reserving human expertise for cases requiring thoroughness.
4Productivity
If automatic mapping without user review is used, then configuration speed increases, but user ability to validate and correct errors is lost
Solution Approach 1:
The system provides dynamic control options where users can adjust the level of automatic mapping and review based on their needs. Users can configure the system to perform quick automatic mapping when speed is prioritized, or enable more thorough review processes when accuracy and validation are more important, making the system adaptable to different operational contexts.
Data Source
AI summary
Aspects of the present disclosure involve a system including a computer-readable storage medium storing a program and method for providing interfaces for automatically mapping webpages to page groups. The program and method provide for determining prioritized terms for plural URLs corresponding to webpages of a website; sending, to a user's client device, an indication of the prioritized terms; receiving, from the client device, an indication of the prioritized terms as modified by the user; generating page groups based on the modified prioritized terms, each page group being assigned to a page category for the website, and each page group having URL-based conditions; sending, to the client device, an indication of the page groups; receiving, from the client device, an indication of the page groups as modified by the user; and causing display of a mapping interface on the client device, the mapping interface corresponding to the modified page groups.