Merchant Store Recommendation Engine for Virtual Currency
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Solution Overview
Problem
Conventional methods fail to effectively recommend a merchant store for purchasing virtual currency instruments, particularly when the merchant's location is not preferred by the recipient due to distance or other factors, leading to suboptimal gift choices.
Innovation Solution
A system and method that utilizes a server and electronic device to generate recommendations for merchant stores based on user preferences and transaction data, including frequency of usage, monetary value, location, and time of usage, to provide customized virtual currency instrument recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a user selects a virtual currency instrument from any merchant store, then the user can complete the transaction quickly, but the recipient may not prefer the merchant store due to location or other factors
Solution Approach 1:
The system collects transaction data including recipient preferences, location information, and usage patterns. This feedback is processed to generate personalized merchant store recommendations that balance transaction efficiency with recipient preference satisfaction.
Solution Approach 2:
The system dynamically adjusts recommendation parameters based on multiple factors including location distance, transaction frequency, and recipient preferences. By changing these parameters, the system optimizes the balance between quick transaction completion and recipient satisfaction.
2Ease of operation
If the system provides personalized recommendations based on multiple criteria, then recipient preference satisfaction improves, but the system complexity increases
Solution Approach 1:
The system pre-processes and stores user preferences, transaction histories, and merchant information in databases. This preliminary action allows the recommendation engine to quickly retrieve and process data without complex real-time computations, reducing system complexity while maintaining personalization.
Solution Approach 2:
The patent introduces a recommendation engine as an intermediary component that sits between the user interface and the transaction processing system. This mediator handles the complex analysis of multiple criteria and translates it into simple, actionable recommendations, isolating the complexity from the rest of the system.
3Measurement precision
If the system collects and analyzes extensive transaction data, then recommendation accuracy improves, but the data processing time and computational resources increase
Solution Approach 1:
The system segments transaction data into different categories such as location-based data, preference data, and transaction history data. Each segment is processed and stored separately, allowing for efficient retrieval and analysis without processing the entire dataset each time, thus reducing processing time while maintaining accuracy.
Solution Approach 2:
The system performs preliminary data cleaning, validation, and organization during data collection. This pre-processing ensures that data is ready for analysis when needed, reducing the computational burden and processing time during recommendation generation while maintaining high accuracy.
Data Source
AI summary
Various aspects of a method and system to provide a recommendation for selection of a merchant store are disclosed herein. The method includes reception, by a server, of a recommendation request for selection of one or more merchant stores from a plurality of merchant stores. The recommendation request is associated with a beneficiary. The method further includes determination, at the server, of a score associated with each of the plurality of merchant stores based on a plurality of weighted parameters associated with the beneficiary. The method further includes generation of a recommendation, at the server, for the selection of the one or more merchant stores based on the determined score associated with each of the plurality of merchant stores.