Route Recommendation Model Weight Adjustment
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Solution Overview
Problem
Current map-based navigation applications do not always recommend routes that users prefer, as they are based on predetermined factors and may not reflect the most popular or user-preferred routes.
Innovation Solution
A route recommendation model is trained using historical routes and road network data, adjusting weights of links to prioritize popular routes, allowing for a more user-centric route suggestion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If predetermined factors and rules (e.g., time to be consumed, route length, traffic condition) are used for route recommendation, then the route planning process is simple and fast, but the recommended route may not align with user preferences
Solution Approach 1:
The system performs preliminary actions by collecting historical route data and training the recommendation model in advance. The model is pre-trained with user behavior patterns, historical route choices, and road network characteristics before actual route recommendations are provided. This allows the system to make accurate recommendations without complex real-time analysis, resolving the contradiction between recommendation accuracy and system complexity.
Solution Approach 2:
The system creates a virtual copy of user behavior patterns through the trained recommendation model. Instead of directly analyzing complex user preferences and historical data during route planning, the system uses a trained model that replicates user decision-making patterns. This copying approach enables accurate recommendations while keeping the actual route planning process simple and fast.
2Measurement precision
If historical routes and user behavior data are analyzed to improve route recommendation accuracy, then user preference alignment improves, but the processing time and computational resources increase
Solution Approach 1:
The system performs the computationally intensive analysis of historical routes and user behavior data in advance during the model training phase. Once trained, the model can provide accurate recommendations quickly without requiring real-time analysis of historical data. This preliminary action resolves the contradiction by separating the heavy computational work from the time-sensitive recommendation process.
Solution Approach 2:
The system dynamically adapts the level of analysis based on the specific routing scenario. For common route planning tasks, the pre-trained model provides quick recommendations. For more complex or unusual routes, the system can perform additional analysis. This dynamic approach balances recommendation precision with planning time, adjusting the computational effort to match the actual needs of each routing query.
Data Source
AI summary
A method for recommending a route includes obtaining a first start point and a first end point relating to a road network. The method also includes obtaining a route recommendation model. The method further includes determining a recommendation route from the first start point to the first end point based on the route recommendation model.