Shopping Pattern Recognition for Grocery Recommendations
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Solution Overview
Problem
Existing recommendation techniques fail to consider the specific and recurring purchasing patterns of customers for grocery products, which are unique to individuals and influenced by product types and store-specific habits.
Innovation Solution
A method and system for shopping pattern recognition that automatically derives customer shopping patterns from transaction histories, analyzing per-product purchases and frequencies, as well as day-of-the-week patterns, to provide personalized recommendations and improve customer interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing recommendation techniques are used, then general product recommendations can be provided, but they fail to consider specific recurring purchasing patterns of customers for grocery products
Solution Approach 1:
The patent segments the recommendation system into multiple components: a pattern identification module that analyzes transaction histories to discover recurring purchasing patterns, and a recommendation module that uses these patterns to generate personalized suggestions. This segmentation allows the system to focus on specific aspects of customer behavior separately, improving recommendation accuracy without overwhelming complexity.
Solution Approach 2:
The system performs preliminary action by pre-analyzing transaction histories to identify recurring purchasing patterns before making recommendations. The pattern identification module continuously processes historical data in the background, building a profile of customer behavior that is then applied when generating recommendations, rather than analyzing patterns in real-time during the recommendation process.
2Measurement precision
If personalized recommendations based on detailed transaction history analysis are implemented, then recommendation accuracy improves, but system complexity and data processing requirements increase
Solution Approach 1:
The patent extracts only the essential and relevant features from complete transaction histories - specifically recurring purchasing patterns, frequency of purchases, and temporal relationships between product purchases. By extracting only these key pattern elements rather than processing all raw transaction data, the system achieves high recommendation accuracy while reducing computational complexity and data processing requirements.
Solution Approach 2:
The system changes parameters by transforming raw transaction data into pattern-based representations. Instead of working with complete transaction records, the system converts data into recurring pattern identifiers, purchase frequencies, and temporal patterns. This parameter transformation simplifies the data structure while preserving the essential information needed for accurate recommendations.
3Loss of information
If comprehensive transaction history mining is performed to derive shopping patterns, then recommendation relevance improves, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary action by continuously pre-processing transaction histories in the background to identify and store recurring purchasing patterns before they are needed for recommendations. The pattern identification module operates on historical data independently, building a repository of customer patterns that can be quickly retrieved and applied during recommendation generation, minimizing real-time processing delays.
Solution Approach 2:
The patent uses copying by creating simplified pattern representations from complete transaction histories. Instead of storing and re-analyzing entire transaction records, the system creates compact copies in the form of recurring pattern identifiers and purchase frequency metrics. These pattern copies capture the essential purchasing behavior information while occupying minimal storage space and enabling rapid retrieval for recommendation purposes.
Data Source
AI summary
Shopping patterns for a customer with respect to a particular product that is repeatedly purchased by the customer with a given frequency are recognized across multiple communication channels by mining transaction histories for the customer over each of the channels. The shopping patterns are processed on a per-communication channel basis for making recommendations, providing reminders, and/or providing default shopping lists on behalf of the customer.