Map Information Display Using Click Probability Recommendations
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Solution Overview
Problem
Current map information display systems lack the ability to actively recommend relevant information beyond a predetermined geographical range, reducing user efficiency in information acquisition.
Innovation Solution
A method that acquires user features and historical click theme information to determine click probabilities using a pre-trained recommendation model, displaying recommended themes with meeting a predetermined requirement on the map, enhancing personalized recommendations and enriching map content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If map information is limited to predetermined geographical range, then system complexity is reduced, but information completeness deteriorates
Solution Approach 1:
The system pre-calculates and stores recommendation strategies offline before user interaction. The offline calculation module pre-processes map information, user preferences, and recommendation rules to generate candidate recommendation strategies, which are then quickly retrieved and applied during online map usage. This preliminary action reduces online computational complexity while maintaining comprehensive information coverage.
Solution Approach 2:
The recommendation system is divided into multiple independent modules: offline calculation module, online calculation module, and display module. Each module handles specific tasks separately - offline module does heavy computation, online module handles real-time user interaction, and display module presents results. This segmentation allows comprehensive information processing without overwhelming system complexity at any single point.
2Productivity
If personalized recommendation is implemented, then user information acquisition efficiency is improved, but device complexity increases
Solution Approach 1:
The system automatically analyzes user click behavior, preferences, and historical data without requiring manual user configuration. The recommendation model self-adjusts based on user interactions, automatically generating personalized recommendations. This self-service approach improves user efficiency while avoiding the complexity of manual personalization setup and management.
Solution Approach 2:
The system continuously monitors user click behavior and feedback on recommended themes, using this information to refine and adjust recommendation strategies. The online calculation module receives real-time user interaction data and adjusts recommendations dynamically. This feedback mechanism enables personalized recommendations to adapt to user preferences while maintaining manageable system complexity through iterative improvement rather than complex upfront design.
3Loss of information
If comprehensive map information is displayed, then information completeness is improved, but ease of operation deteriorates
Solution Approach 1:
The system displays different information densities in different map regions based on user relevance. Highly relevant themes recommended by the model are displayed with prominent markers and detailed information, while less relevant areas maintain standard display. This local quality approach ensures comprehensive information is available where needed without overwhelming the user in all areas, maintaining ease of operation while improving information completeness.
Solution Approach 2:
The system displays a curated subset of the most relevant themes from the comprehensive map information, rather than displaying all available information uniformly. The recommendation model identifies and highlights the most valuable themes based on user preferences and behavior, providing partial display of comprehensive data. This approach maintains ease of operation by avoiding information overload while ensuring the most important information is prominently displayed.
Data Source
AI summary
The present application discloses a method for displaying map information and corresponding apparatus, electronic device, and computer storage medium, which relates to the fields of deep learning, knowledge graphs, and artificial intelligence. The method may include: when a user starts a map, acquiring a user feature of the user and historical click theme information of the user; for any to-be-recommended theme, determining a click probability of the to-be-recommended theme by using a pre-trained recommendation model according to the user feature and the historical click theme information respectively; and displaying the to-be-recommended themes with the click probabilities meeting a predetermined requirement on the map. The efficiency of acquiring information through maps by users and the like can be improved by use of the solutions according to the present application.