Sequential Prediction Tree for Purchase Pattern Analysis
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Solution Overview
Problem
Existing product recommendation approaches fail to account for a customer's historical sequence of purchases and non-purchase trends, leading to inefficient resource allocation and ineffective sales strategies, as they do not differentiate between cyclical and occasional purchasing patterns, nor predict the sequence of future purchases accurately.
Innovation Solution
Implementing a concerted learning and multi-instance sequential prediction tree, which captures historical purchase patterns and sequences to provide accurate predictions of future purchases by analyzing the most recent purchases of similar customers and computing scores for immediate future purchase periods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing product recommendation approaches are used, then resource allocation and sales strategies can be implemented, but they fail to account for historical purchase sequences and non-purchase trends, leading to ineffective recommendations
Solution Approach 1:
The patent segments the purchase prediction process into distinct components: sequential recommendation operation that analyzes purchase sequences, concerted learning operation that integrates multiple data sources, and multi-instance learning that handles multiple purchase instances. This segmentation allows each component to specialize in specific aspects of pattern recognition, improving overall prediction accuracy while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
The patent introduces temporal dimensionality by analyzing sequences of purchases over time rather than isolated purchase events. The sequential recommendation operation captures temporal patterns and trends across multiple purchase instances, adding a time-based dimension to the recommendation system that enables differentiation between cyclical and occasional purchasing patterns.
2Measurement precision
If sequential purchase patterns are analyzed in detail, then prediction accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing purchase sequence patterns in a structured format that enables efficient querying. The sequential recommendation operation pre-analyzes historical purchase data to identify recurring patterns and trends, storing these insights for rapid retrieval during prediction operations, thus reducing real-time processing requirements.
Solution Approach 2:
The patent uses copying by creating simplified representations of complex purchase sequences. The multi-instance sequential recommendation captures essential patterns from multiple purchase instances and creates condensed model instances that preserve predictive information while requiring less computational resources for analysis, enabling faster processing without sacrificing accuracy.
Data Source
AI summary
A method, system and computer readable medium for performing a purchase prediction operation. The purchase prediction operation includes: selecting a target purchaser, the purchase prediction operation providing a purchase prediction for the target purchaser; capturing a product term associated with a most recent purchase period of the target purchaser; performing a sequential recommendation operation, the sequential recommendation operation providing a sequence recommendation score; and, generating a purchase pattern prediction for the target user based upon the sequential recommendation score.