Merchant Correspondence Matrix for Recommendation Accuracy
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Solution Overview
Problem
Existing recommender systems struggle to provide meaningful recommendations for users without a personal profile of preferences, especially when it comes to merchants.
Innovation Solution
A method and system that utilize financial transaction data to generate a merchant correspondence matrix, which includes indicators of interactions between merchants based on shared customers. This matrix is used to infer user preferences and generate ranked lists of merchants for recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional recommender systems use historical data to determine user preferences, then personalized recommendations can be provided for users with established profiles, but meaningful recommendations cannot be provided for users without personal preference profiles
Solution Approach 1:
The patent introduces merchant correspondence matrices as an intermediary structure that mediates between available transaction data and recommendation generation. Instead of directly inferring user preferences from sparse data, the system uses merchant-merchant relationship matrices as a bridge, allowing recommendations to be generated based on merchant associations rather than requiring direct user preference information.
Solution Approach 2:
The system uses readily available financial transaction data that would otherwise be discarded or underutilized. By leveraging existing transaction records between merchants and cardholders, the system creates value without requiring additional expensive data collection or user profiling efforts.
2Measurement precision
If recommender systems rely on personal user profiles, then accurate recommendations can be generated, but the system complexity increases due to profile maintenance and data collection requirements
Solution Approach 1:
The patent extracts the core recommendation function from the user profile context. Instead of building complex user profiles, the system extracts merchant correspondence relationships from transaction data and uses these extracted patterns directly for recommendations, eliminating the need for complex profile maintenance infrastructure.
Solution Approach 2:
The merchant correspondence matrices serve multiple functions: they enable recommendations for new users, provide fraud detection capabilities, and work with any user regardless of their transaction history. This multi-functionality reduces system complexity by using a single data structure for multiple purposes.
3Adaptability or versatility
If financial transaction data is used to generate merchant correspondence matrices, then personalized recommendations can be provided without prior user preference knowledge, but data privacy concerns arise from analyzing detailed transaction information
Solution Approach 1:
The system creates aggregated merchant correspondence matrices that copy and generalize transaction patterns without storing or processing individual user transaction details. The matrices represent statistical relationships between merchants across the user base, allowing personalization without exposing individual privacy.
Solution Approach 2:
The patent segments the transaction data analysis into two levels: individual user transactions are processed privately to generate contribution data, which is then aggregated into population-level merchant correspondence matrices. This segmentation allows personalized recommendations while protecting individual privacy through aggregation.
Data Source
AI summary
A method and system for recommending a merchant are provided. The method includes receiving financial transaction data documenting financial transactions between a plurality of account holders and a plurality of merchants and generating a merchant correspondence matrix that includes the plurality of merchants and a plurality of indicators of interactions associated with pairs of the plurality of merchants. The plurality of indicators of interactions tallying financial transactions conducted by the plurality of account holders at both of the merchants in a pair of the plurality of merchants. The method further includes receiving a query for a recommendation of a merchant from an account holder and generating a ranked list of merchants based on a recommender algorithm. The recommender algorithm inferring user preferences from attributes of the plurality of merchants that were visited by the cardholder.