Tip Amount Weighting for Merchant Recommendation Accuracy
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current metrics for recommending merchants, such as restaurants and services, are often inaccurate due to confounding variables, leading to recommendations that do not accurately reflect the true value of customer experience.
Innovation Solution
A method and system that categorize tip amounts based on customer tipping consistency using machine learning, assigning higher weights to tip amounts that deviate from a customer's usual range, allowing for a more accurate scoring and recommendation of merchants based on the quality of service.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If tip amounts are used directly to score merchants, then the scoring process is simple, but the recommendation accuracy is low due to confounding variables
Solution Approach 1:
The patent segments tip amounts into different categories (e.g., small, medium, large tips) and applies different weights to each category based on customer tipping consistency. This segmentation allows the system to differentiate between routine tips and exceptional tips, improving measurement precision without requiring an overly complex model.
Solution Approach 2:
The system performs preliminary analysis by categorizing customers into consistent tipers and variable tipers before scoring merchants. This preliminary action allows the system to adjust weights based on customer behavior patterns, improving recommendation accuracy while keeping the overall framework manageable.
2Reliability
If all tip amounts are weighted equally, then the calculation is straightforward, but the reliability of recommendations is low due to inconsistent tipping behavior
Solution Approach 1:
The patent applies local quality by assigning different weights to tip amounts based on the specific customer's tipping consistency. Consistent tipers have their tips weighted differently than variable tipers, allowing the system to account for individual behavior patterns and improve recommendation reliability.
Solution Approach 2:
The system changes the parameter of tip weight dynamically based on customer categorization. By adjusting weights according to whether a customer is a consistent or variable tiper, the system improves reliability while maintaining a relatively simple weighting model that can be implemented with standard computational resources.
3Measurement precision
If the system analyzes detailed customer tipping patterns, then recommendation accuracy improves, but the data processing complexity increases
Solution Approach 1:
The patent extracts only the essential feature from customer tipping data - whether the customer is a consistent tiper or variable tiper. By focusing on this single extracted characteristic rather than analyzing every aspect of tipping behavior, the system achieves good measurement precision without excessive data processing complexity.
Data Source
AI summary
A method, system, and non-transitory computer readable medium are disclosed for recommending merchants. The method includes storing a first tip amount and a second tip amount corresponding to a first merchant and a second merchant, respectively. Then, categorizing the first tip amount and the second tip amount into a lower weighted category or higher weighted category. The method also includes generating, using a weighted model that includes machine learning logic: a first score for the first merchant based on the first tip amount and the category of the first tip amount; and a second score for the second merchant based on the second tip amount and the category of the second tip amount. Tip amounts in the higher weighted category carry more weight in generating scores than those in the lower weighted category. Then, either the first merchant or the second merchant is recommended based on the scores.