Classification Model Training Using User Behavior Data
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Solution Overview
Problem
Existing classification methods rely on pre-defined identification rules that are prone to low accuracy due to subjective influences and experiential biases, resulting in inaccurate user categorization.
Innovation Solution
A method for training a classification model that utilizes behavior information and personal basic information of users to learn relationships between influence factors and categories, thereby improving the accuracy of user categorization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pre-defined identification rules are used for user classification, then the classification process is simple and fast, but the accuracy of classification results is low due to subjective influences and experiential biases
Solution Approach 1:
The patent replaces the mechanical system of manual rule creation with an automated machine learning classification model. The model learns classification boundaries automatically from training data containing user behavior information and category labels, eliminating the need for manual rule definition by domain experts. This substitution transforms the classification process from a subjective, experience-based mechanical system to an objective, data-driven automated system.
Solution Approach 2:
The patent changes the parameters used for classification from manually defined rule parameters to learned model parameters. Instead of using fixed thresholds and conditions set by experts, the classification model learns optimal parameters from training data, including behavior information features and category relationships. This parameter transformation enables the system to adapt to different classification scenarios by training on specific domain data.
2Reliability
If manual analysis by domain experts is used to create identification rules, then the classification process is interpretable and controllable, but the accuracy is limited by the experience and subjective factors of the staff
Solution Approach 1:
The patent implements self-service by enabling the classification model to automatically learn and optimize its own parameters from training data. The system performs self-training on labeled user behavior information, automatically identifying important features and classification boundaries without continuous human intervention. This self-service capability allows the model to improve its reliability through automated learning from real-world data patterns.
Solution Approach 2:
The patent incorporates feedback mechanisms through the training process, where the model learns from labeled training data containing actual user categories. The feedback from training examples allows the model to adjust its parameters and improve classification reliability iteratively. During deployment, the model can also learn from prediction results and continue to refine its performance through continuous feedback loops.
Data Source
AI summary
The present application discloses a method for training a classification model, a classification method, an apparatus and a device. A specific implementation is: acquiring behavior information of multiple users and personal basic information of the multiple users; where categories of at least part of users of the multiple users are known; inputting the personal basic information of the multiple users into a classification model to be trained to obtain feature information of the multiple users and predicted categories of users with known categories; and training the classification model to be trained according to the behavior information of the multiple users, the feature information of the multiple users, the predicted categories of the users with the known categories, and real categories of the users with the known categories, to obtain a trained classification model. The user categories determined by using the classification model are more accurate.