Personalized Text Report Category Sorting
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Solution Overview
Problem
Users often select inappropriate text report categories when submitting complaints or feedback, leading to inefficiencies in processing and inaccuracies in data mining due to the lack of personalized categorization.
Innovation Solution
A method and apparatus that utilize a personalized model trained with user behavior history and personal information to evaluate and sort text report categories, ensuring a more accurate selection by determining the order of categories based on personalized evaluation results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a fixed category list is provided for text report submission, then the system structure is simple, but users often select inappropriate categories leading to processing inefficiency
Solution Approach 1:
The system performs preliminary action by pre-training a personalized classification model using user behavior history and personal information before the user submits a text report. The model predicts and sorts categories in advance based on the user's profile, so that when the user selects a category, it is already optimized for their needs, improving processing efficiency without requiring complex real-time analysis during submission
Solution Approach 2:
The system implements self-service by automatically generating personalized category sortings based on user behavior history and personal information stored in the database. The classification model autonomously analyzes user patterns and adjusts category orders without requiring manual intervention or complex user input, thereby improving processing efficiency while maintaining manageable system complexity through automated routines
2Measurement precision
If categories are sorted based on general statistics, then the system is simple to implement, but data mining accuracy is reduced due to lack of personalization
Solution Approach 1:
The system applies local quality by transitioning from a uniform category sorting approach to a personalized approach where each user receives a customized category order based on their specific behavior history and personal information. The classification model analyzes individual user patterns and adjusts category sortings locally for each user, thereby improving data mining accuracy while managing complexity through targeted personalization rather than complete system redesign
Solution Approach 2:
The system implements parameter changes by dynamically adjusting category sorting parameters based on user behavior history and personal information. The classification model uses these parameters to generate personalized category orders, transforming the static general statistics into dynamic user-specific parameters. This improves data mining accuracy by capturing individual user patterns while controlling complexity through parameter adjustment rather than fundamental system complexity
3Loss of time
If users manually select from all categories, then the system remains simple, but the time required for category selection increases
Solution Approach 1:
The system performs preliminary action by pre-calculating and presenting the top predicted categories to the user before they need to make a selection. The classification model analyzes user behavior history and personal information to predict the most likely correct category in advance, so that users only need to confirm or make minor adjustments rather than searching through all categories, significantly reducing selection time while maintaining manageable system complexity through efficient pre-processing
Data Source
AI summary
A computer-implemented method includes: receiving, by a computing device, a text report request from a user device associated with a user; obtaining a behavior history and personal information of the user; inputting the behavior history and the personal information of the user into a model, to obtain a plurality of personalized evaluation results, each personalized evaluation result corresponding to a respective text report category of a plurality of text report categories, in which each personalized evaluation result indicates a predicted relevance of the corresponding text report category to a problem faced by the user, and in which the model includes a classification model trained using one or more supervised learning techniques on a plurality of user behavior history samples and a plurality of personal information samples; and determining an order in which the plurality of text report categories are to be presented to the user.