Persona-Based Merchant Recommendation Scoring
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Solution Overview
Problem
The proliferation of online marketplaces has led to challenges in providing consumers with relevant and tailored offers, as existing systems often provide generic recommendations that are ignored due to their lack of relevance, resulting in lower credibility and consumer disinterest.
Innovation Solution
A system and method that processes an analysis cycle to determine interest merchants by selecting seed merchants, identifying consumers who have transacted with them, scoring merchants based on network connectivity and activity, and updating scores to provide relevant recommendations to consumers, while also allowing for persona-based merchant selection and ranking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If generic merchant recommendations are provided to consumers, then the system complexity is reduced, but the relevance and credibility of recommendations deteriorates
Solution Approach 1:
The patent segments consumers into distinct personas based on their transaction behaviors, interests, and preferences. By dividing the consumer base into segmented personas rather than treating all consumers uniformly, the system can provide personalized recommendations without requiring overly complex individual analysis of each consumer. This segmentation approach resolves the contradiction by enabling relevant recommendations through group-based personalization.
Solution Approach 2:
The patent performs preliminary analysis to determine consumer personas and merchant affinities before making recommendations. By pre-processing consumer transaction data to establish persona profiles and merchant relationships in advance, the system prepares personalized recommendation frameworks beforehand. This preliminary action enables credible recommendations to be generated efficiently when needed, without requiring complex real-time analysis.
2Reliability
If personalized merchant recommendations are provided to consumers, then the relevance and engagement increases, but the data processing complexity and computational resources increase
Solution Approach 1:
The patent creates universal persona profiles that can be applied across multiple contexts and recommendation scenarios. By developing multi-functional persona models that capture essential consumer characteristics and preferences, the system can generate personalized recommendations for different merchants and situations using the same foundational persona data. This universality reduces the need for separate complex analysis for each recommendation query.
Solution Approach 2:
The system enables personas and merchant affinities to serve themselves by automatically analyzing transaction data and establishing relationships without requiring continuous external intervention. Once consumer behavior patterns are established, the persona profiles self-update and self-maintain based on ongoing transactions, reducing the computational burden of continuous complex analysis while maintaining recommendation relevance.
3Measurement precision
If comprehensive consumer transaction data is analyzed, then the accuracy of persona determination improves, but the time and computational resources required increase
Solution Approach 1:
The patent applies partial analysis by focusing on key transaction attributes and behaviors that are most indicative of consumer personas, rather than analyzing every single transaction detail equally. By identifying and analyzing only the most relevant transaction characteristics, the system achieves sufficient persona accuracy without the time and computational cost of exhaustive data processing. This selective partial action resolves the contradiction between accuracy and processing time.
4Measurement precision
If merchants are scored based on multiple criteria (network connectivity, activity, over-index), then the recommendation accuracy improves, but the scoring complexity increases
Solution Approach 1:
The patent transforms multiple complex scoring criteria into standardized, normalized parameters that can be systematically compared and combined. By converting diverse merchant attributes (network connectivity, activity levels, over-index metrics) into uniform scored parameters, the system maintains high scoring accuracy while reducing the complexity of handling and processing multiple different data types. This parameter standardization enables accurate multi-criteria scoring without proportionally increasing system complexity.
Data Source
AI summary
The method of processing an analysis cycle to determine interest merchants may include selecting a seed merchant relevant to a topic interest, identifying consumers that have completed a transaction with the seed merchant to generate a list of identified consumers, determining merchants visited by the identified consumers, scoring all the merchants based on network connectivity, activity, and merchant over-index, updating the seed merchant in response to the list of scored merchants relative to a scoring threshold, and scoring the list of identified consumers based on the number of distinct merchants in transaction and over-indexing. Additionally, the method may further comprise producing a list of updated interest merchants and a list of updated identified consumers, where the updated interest merchants and the updated identified consumers are relevant to the topic interest.