User Purchase Limit Prediction with Multi-Branch Regression
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing neural network models for purchase prediction on users consider only a single factor, leading to inaccurate final purchase limits due to insufficient comprehensive consideration of factors.
Innovation Solution
A method involving a target model that performs multiple types of regression processing, including feature extraction and multiple regression branches, to obtain comprehensive purchase probabilities and limits, enhancing accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a neural network model uses only a single regression processing to determine the final purchase limit, then the model structure is simple, but the prediction accuracy is insufficient
Solution Approach 1:
The patent divides the prediction task into multiple independent regression branches, each handling different aspects of purchase limit prediction. This segmentation allows each branch to specialize in specific factors (e.g., user behavior patterns, product characteristics, contextual information), improving overall prediction accuracy while maintaining manageable model complexity through modular architecture
Solution Approach 2:
The patent implements a multi-functional regression model that simultaneously performs multiple types of regression processing (e.g., linear regression, logistic regression, decision tree regression) within a single unified model structure. This multi-functionality enables the model to consider diverse factors and prediction approaches concurrently, enhancing prediction accuracy without requiring separate models for each factor
2Measurement precision
If the model considers only one factor for purchase limit determination, then the model is simple and easy to implement, but the prediction accuracy is not high enough
Solution Approach 1:
The patent transitions from single-factor prediction to multi-dimensional prediction by introducing multiple regression branches that each process different dimensions of data (e.g., temporal patterns, spatial relationships, categorical attributes, continuous variables). This dimensional expansion allows the model to capture complex interactions between factors, significantly improving prediction accuracy while maintaining adaptability through a systematic multi-dimensional framework
Data Source
Figure 1
Figure 2a
Figure 2b
AI summary
A purchase prediction method and a related device thereof are provided. The method includes: obtaining information associated with a user (501); performing feature extraction processing on the information, to obtain a purchase probability of the user (502); performing first regression processing on the information, to obtain a first purchase limit of the user (503), and performing second regression processing on the information, to obtain a second purchase limit of the user (504), where the first regression processing and the second regression processing are different regression processing; and finally obtaining a predicted purchase limit of the user based on the purchase probability, the first purchase limit, and the second purchase limit (505).