User Grouping via Data Standardization and K-Means Clustering
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Solution Overview
Problem
Existing methods for grouping network service users are inefficient due to random user grouping, which leads to unreasonable information push and confusion, as they fail to adapt to changing user interests and behaviors.
Innovation Solution
A method and apparatus that acquire and standardize user attribute and behavior data, determine group central points, and push targeted information based on group features, using a k-means clustering algorithm and offset probability calculations to form precise user groups.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If random push or general push is used, then implementation is simple, but information push efficiency is low
Solution Approach 1:
The patent changes the parameter of user grouping from random/experience-based to data-driven with multiple dimensions (user attributes, behavior data, interest preferences). By transforming the grouping parameters into standardized data and using offset probability calculations, the system achieves precise user segmentation that improves information push efficiency while maintaining automated operation.
Solution Approach 2:
The patent implements dynamic user grouping that adapts to changing user interests and behaviors over time. The system continuously updates user data and recalculates group assignments based on current behavior patterns, making the grouping dynamic rather than static. This allows the system to adapt to user changes while maintaining efficient information push.
2Adaptability or versatility
If experience-based user grouping is used, then carrier expertise is utilized, but adaptability to changing user interests is poor
Solution Approach 1:
The patent implements a feedback mechanism where user behavior data and responses to information push are continuously collected and used to refine user grouping. The system calculates offset probabilities based on user attributes and behaviors, and this feedback loop enables the system to adapt to changing user interests while improving grouping precision through data-driven adjustments.
Solution Approach 2:
The system enables users to effectively 'self-categorize' into groups based on their own behavior patterns and preferences. By analyzing user-generated data such as browsing history, service usage, and interaction patterns, the system automatically assigns users to appropriate groups without manual intervention, achieving both adaptability and precision.
3Measurement precision
If conventional grouping methods are used, then operational complexity is low, but grouping precision is insufficient
Solution Approach 1:
The patent replaces manual/experience-based grouping mechanisms with an automated computational system. The system uses algorithms to calculate offset probabilities, standardize user data, and automatically assign users to groups based on multiple dimensions of user attributes and behaviors. This substitution of mechanical/manual processes with automated computing achieves high precision while managing complexity through systematic algorithms.
Data Source
AI summary
A method for grouping network service users includes acquiring attribute and/or behavior data of multiple users within a current period, and converting the attribute and/or behavior data into standardized data; determining multiple group central points according to the standardized data, and placing the standardized data in a group where a group central point having a shortest distance is located; determining group features of groups according to standardized data in the groups; and separately pushing corresponding service push information to users in the groups according to the group features of the groups. In addition, an apparatus for grouping network service users is further described.