Destination Recommendation Using Driving Pattern Visit Probabilities
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Solution Overview
Problem
Current navigation devices lack effective customization in recommending destinations based on user driving patterns, failing to provide personalized recommendations that improve user satisfaction.
Innovation Solution
A destination recommending apparatus and system that collects user driving pattern information to calculate visit probabilities of POIs, using location and time similarity probabilities, and adjusts these probabilities with an aging rate and weights to recommend destinations based on user behavior and preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If navigation devices provide basic path guidance functions, then navigation reliability is maintained, but recommendation quality and user satisfaction deteriorate due to lack of customization
Solution Approach 1:
The navigation device is divided into distinct functional modules: a basic path guidance module that ensures navigation reliability, and a separate recommendation module that analyzes driving patterns and calculates visit probabilities to provide customized destination recommendations. This segmentation allows each module to optimize its specific function without compromising the other.
Solution Approach 2:
The recommendation system operates autonomously by automatically collecting driving pattern information, calculating visit probabilities for various destinations, and generating personalized recommendations without requiring manual user input. The system learns from historical data and continuously improves recommendations based on calculated probabilities.
2Measurement precision
If the system collects and analyzes extensive driving pattern information to improve recommendation accuracy, then recommendation quality improves, but device complexity and processing requirements worsen
Solution Approach 1:
The complex analysis and calculation functions are extracted from the main navigation device and implemented as separate processing algorithms. The device collects driving pattern information and extracts key features (visit frequencies, time patterns, location preferences) to calculate visit probabilities, separating the computational burden from core navigation functions.
Solution Approach 2:
The system transforms qualitative driving behavior data into quantitative parameters such as visit probability values calculated through mathematical formulas. By converting complex behavioral patterns into standardized probability parameters, the system simplifies analysis while maintaining high recommendation accuracy.
3Measurement precision
If the system calculates visit probabilities based on multiple factors including location and time similarity, then recommendation precision improves, but calculation time and processing load worsen
Solution Approach 1:
The system pre-calculates and stores base probabilities for various destinations based on historical driving patterns. When a recommendation is needed, the system retrieves these pre-calculated values and adjusts them using current location and time similarity factors, avoiding complete recalculation and reducing processing time while maintaining accuracy.
Solution Approach 2:
The system calculates visit probabilities for only the most relevant destinations based on initial filtering criteria such as proximity to current location and time of day. Instead of calculating probabilities for all possible destinations, the system focuses computational resources on a subset of likely candidates, reducing calculation time while maintaining recommendation quality.
Data Source
AI summary
A destination recommending apparatus includes: a navigation device configured to collect driving pattern information of a user; and a controller configured to calculate a visit probability of a destination at a current location or the visit probability of the destination at a current time, based on the driving pattern information. The controller is configured to predict the destination based on the visit probability.