On-Demand Service Location Recommendation Engine
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Solution Overview
Problem
Existing on-demand service platforms face challenges in efficiently recommending service locations for on-demand services, such as taxi hailing and delivery services, as they lack effective methods to utilize historical service data to optimize location recommendations based on user preferences and usage patterns.
Innovation Solution
The system determines the recommended service location by selecting from historical on-demand services the most recently performed or highest frequency of use locations, and if no historical data exists, it identifies locations within a predetermined distance from the current default location based on usage frequency, using a processing engine to analyze and rank potential service locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system uses basic location recommendation without historical data analysis, then the system complexity is low, but the service location recommendation accuracy is poor
Solution Approach 1:
The system pre-processes and stores historical service location data in advance, organizing it into structured formats that can be quickly queried and analyzed. This preliminary organization of data enables accurate real-time recommendations without requiring complex processing during the actual service request, thus improving recommendation accuracy while maintaining manageable system complexity
Solution Approach 2:
The patent introduces a recommendation engine as an intermediary component between the service request and the location selection. This engine acts as a mediator that analyzes historical data, service types, and user preferences to generate optimized location recommendations, thereby improving accuracy without significantly increasing overall system complexity through modular architecture
2Measurement precision
If the system analyzes comprehensive historical service data to determine recommended locations, then the recommendation accuracy improves, but the processing time increases
Solution Approach 1:
The system extracts only the most relevant features from historical service data, such as frequently visited locations, service type patterns, and user preference indicators, rather than processing the entire historical dataset. This selective extraction maintains high recommendation accuracy by focusing on key predictive factors while significantly reducing processing time through diminished data volume
Solution Approach 2:
The patent implements a tiered analysis approach where the system performs partial analysis of historical data based on the specific service context. For common service types with abundant historical data, it uses comprehensive analysis for high accuracy, while for less common services or time-critical requests, it applies simplified analysis methods that require less processing time but still provide satisfactory recommendations
3Ease of operation
If the system recommends locations based on user historical behavior patterns, then user satisfaction improves, but the device complexity increases
Solution Approach 1:
The system automatically analyzes user historical behavior patterns and generates personalized location recommendations without requiring explicit user input or configuration. Users simply need to provide their service requests, and the system autonomously processes their historical data to infer preferences and habits, thereby improving user satisfaction while keeping the interface simple and the processing complexity manageable through automated algorithms
Data Source
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AI summary
Systems and methods are proposed for recommending a recommended service location for an on-demand service. The systems may perform the methods to obtain a request of an on-demand service including a current default service location through a wireless network; determine whether at least one historical on-demand service exists, each of the at least one historical on-demand service having a historical default service location within a first predetermined distance from the current default service location; upon existence of the at least one historical on-demand service, determine a currently recommended service location of the request based on the at least one historical on-demand service; and direct the requester terminal to display the currently recommended service location of the request.