Merchant Recommendation Engine Using Neutral Preference Comparison
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Solution Overview
Problem
Consumers face challenges in selecting merchants due to the overwhelming number of options and unreliable consumer reviews, which can be biased, and merchants struggle to effectively promote their services to cardholders in an objective and efficient manner.
Innovation Solution
A computer system and method that utilizes transaction data and cardholder preferences to rank and recommend merchants by comparing individual cardholder preferences with neutral cardholder preferences, providing a merchant score that balances personal preferences with general popularity, thus offering objective recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If consumers rely on consumer reviews to select merchants, then they gain additional opinions and information, but the reviews become unreliable due to different consumer preferences and potential bias from negative experiences
Solution Approach 1:
The patent introduces a neutral third-party system (the recommendation engine) that mediates between merchants and consumers. This system processes transaction data objectively and generates recommendations based on aggregated neutral cardholder preferences rather than relying on potentially biased consumer reviews. The intermediary system filters and processes information to eliminate subjectivity while preserving useful recommendations.
Solution Approach 2:
The patent changes the parameters used for merchant evaluation from subjective consumer opinions to objective transaction-based metrics. By analyzing actual purchase behavior data and neutral cardholder preferences, the system transforms the basis of recommendation from qualitative reviews to quantitative transaction patterns, thereby improving reliability while maintaining information value.
2Loss of information
If consumers search through multiple websites and friend numerous merchants to make informed decisions, then they gain more information, but the process becomes time-consuming
Solution Approach 1:
The patent merges multiple information sources and processing functions into a single integrated recommendation system. Instead of requiring consumers to visit multiple merchant websites and aggregate information themselves, the system consolidates transaction data, preference analysis, and recommendation generation in one place, dramatically reducing the time required while maintaining comprehensive information quality.
Solution Approach 2:
The system performs preliminary analysis of merchant information and cardholder preferences in advance, so that when a consumer needs a recommendation, the work is already done. The recommendation engine pre-processes transaction data and maintains updated merchant rankings based on neutral cardholder preferences, eliminating the need for consumers to perform time-consuming research at the moment of decision.
3Adaptability or versatility
If merchants offer incentives and consumers search through multiple sources, then consumers can make more informed decisions, but the system becomes complex and difficult to navigate
Solution Approach 1:
The patent extracts the complexity of preference analysis and recommendation generation from the consumer's task and places it in the backend system. The system handles the complex processing of transaction data, preference matching, and merchant ranking internally, presenting consumers with simplified, ready-made recommendations that adapt to their preferences without requiring them to navigate complex interfaces or perform complex searches.
4Measurement precision
If the system provides personalized recommendations based on individual cardholder preferences, then recommendation accuracy improves, but the system requires processing and storing large amounts of individual preference data
Solution Approach 1:
The patent segments the preference data into two categories: individual cardholder preferences and neutral aggregate preferences. By separating these data types, the system can efficiently process individualized recommendations while also leveraging aggregated neutral preferences that require less storage and processing overhead. This segmentation allows the system to balance personalization accuracy with data management efficiency.
Data Source
AI summary
A computer system for recommending merchants to a candidate cardholder is provided. The computer system includes a memory device and a processor. The processor receives transaction information for a plurality of cardholders from a payment network. The transaction information includes data relating to purchases made by the cardholders at a plurality of merchants, where the purchases satisfy a first criteria. The processor also receives candidate cardholder preference information for at least one of the merchants input by the candidate cardholder. The processor further determines a merchant rank for each merchant based on the received transaction information and the candidate cardholder preference information, and determines a neutral merchant rank for each merchant based on the received transaction information and neutral cardholder preferences of the plurality of cardholders. The processor also determines a merchant score for each of the plurality of merchants by comparing the merchant rank to the neutral merchant rank.