Social Relationship Determination via Behavior Data Discretization
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Solution Overview
Problem
Existing technologies fail to accurately and efficiently determine complex social relationship types among network users, requiring user participation and leading to decreased e-commerce accuracy and user experience due to the need for voluntary relationship classification and approval processes.
Innovation Solution
A method and apparatus that discretize network social behavior data into levels, calculate merged probabilities of social relationship types based on conditional probabilities, and determine the most likely relationship type without user approval, using initial sample data sets to estimate social relationship types with improved accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If user voluntary approval operation is used to create friend relationship, then relationship accuracy is improved, but time consumption and operation complexity increase
Solution Approach 1:
The system automatically determines social relationship types by analyzing user behavior data without requiring users to manually select or approve relationship classifications. The relationship determination is performed autonomously based on observed interactions, eliminating the need for user participation in the classification process.
Solution Approach 2:
The manual mechanical operation of user approval is replaced with an automated algorithmic system that analyzes behavior patterns. The system substitutes human decision-making with computational analysis of social network behavior data to determine relationship types.
2Measurement precision
If complicated selection process is used to select relationship classification, then relationship type accuracy is improved, but user participation desire decreases
Solution Approach 1:
The system performs relationship classification autonomously without requiring users to engage in selection processes. Users simply need to have their behavior data collected, and the system automatically determines relationship types based on analyzed patterns.
Solution Approach 2:
The system introduces an intermediary analysis layer that processes user behavior data and translates it into relationship classifications. This intermediary process eliminates the need for users to directly interact with relationship selection interfaces.
3Productivity
If mutual behavior data analysis is used to determine relationship type, then automation efficiency is improved, but data processing complexity increases
Solution Approach 1:
The data processing system is segmented into distinct modules: behavior data collection, behavior pattern analysis, probability calculation, and relationship determination. Each module handles specific aspects of the complex processing task independently.
Solution Approach 2:
The system transforms raw behavior data into standardized parameters and probabilities through discretization and statistical analysis. By converting complex behavior patterns into probabilistic parameters, the system simplifies the determination process while maintaining accuracy.
Data Source
AI summary
Social network behavior data of two network subjects whose social relationship type is required to be determined is obtained. The network social behavior data of the two network subjects to be determined is discretized by a preset manner in such a way that each of the social behavior is discretized into N levels according to a quantity of each social network behavior. A merged probability of each social relationship type to which the two network subjects belong is calculated according to a conditional probability that each level of network social behavior corresponds to each social relationship type. A social relationship type having the largest merged probability is initially determined as a social relationship type of the two network subjects. The present techniques effectively utilized network social behavior data, implement determination of social relationship type, and improve user network experiences.