Stop Location Recommendation Engine Using Transaction Data
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Solution Overview
Problem
Current mapping applications fail to provide accurate, personalized recommendations for stop locations along a user's travel path, leading to unhelpful suggestions and missed preferred stops.
Innovation Solution
A computerized method that collects context data from a user's device, identifies stop locations based on travel path and transaction data, determines user preferences from transaction history, and provides tailored recommendations for stop locations, including factors like proximity, opening hours, and wait times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If mapping applications provide general stop location information, then users can see nearby businesses and points of interest, but the recommendations are not personalized and may not match user preferences
Solution Approach 1:
The system performs preliminary action by analyzing user transaction data and determining user preference data in advance, before providing stop recommendations. This allows the system to pre-identify what types of stops the user prefers (e.g., gas stations, restaurants, stores) based on historical transactions, so that when recommendations are needed, the system can immediately filter and present only relevant options matching the user's preferences.
2Ease of operation
If users are shown multiple stop location options, then users have more choices, but it becomes difficult for users to efficiently select preferred locations
Solution Approach 1:
The system extracts and filters stop location information based on user preferences derived from transaction data. Instead of presenting all nearby businesses and points of interest, the system extracts only the stop locations that match the user's identified preferences (such as specific business types, brands, or categories), thereby reducing the number of options the user needs to review while maintaining relevant choices.
3Measurement precision
If the system provides stop recommendations based on travel path, then directions and path information are available, but accurate personalized recommendations are not provided
Solution Approach 1:
The system introduces user preference data as an intermediary element that bridges the gap between travel path information and stop location recommendations. The recommendation engine uses user preference data (derived from transaction history) as a filter or mediator to select appropriate stop locations from the available options along the travel path, thereby achieving accurate personalized recommendations without requiring overly complex analysis of the travel path itself.
Data Source
AI summary
The disclosure facilitates the generation and provision of stop locations based on context data and transaction data associated with a user's account. Context data indicating a location and travel path of a user is collected from a computing device of the user and a plurality of stop locations are identified by a recommendation engine based on the context data. User transaction data associated with the user is received by the recommendation engine. User preference data is determined by the recommendation engine based on the transaction data. The recommendation engine provides a recommendation of at least one stop location based on the user preference data, whereby the user is enabled to select a stop location based on the recommendation. Generating recommendations based on the user's transaction data and context data provides reliable recommendations to the user and enhances the efficiency of the user's stops while traveling.