Semantic Labeling Apparatus for Location-Based Services
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Solution Overview
Problem
Conventional semantic labeling methods for location-based services require users to manually define significance for each location, which is inconvenient and impractical, especially as users visit numerous places, leading to incomplete and inaccurate personalization of services.
Innovation Solution
A semantic labeling apparatus and method that automatically clusters locations based on user behavior patterns, assigns collective semantic labels to groups of similar places, and determines labels for unmarked locations by similarity, reducing user intervention and enhancing personalization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually define semantic labels for each location, then the semantic label accuracy is improved, but the user operation complexity increases
Solution Approach 1:
The system automatically performs semantic labeling by analyzing user behavior patterns, location data, and visit frequencies without requiring manual user input. The processor autonomously generates place attributes, clusters locations, and assigns semantic labels based on detected patterns, making the system self-sufficient and eliminating the need for users to manually define labels for each location.
Solution Approach 2:
The system transforms raw location data into meaningful semantic labels by changing parameters such as visit frequency, duration of stay, time of day, and day of week. These parameter transformations enable automatic differentiation between various types of locations (e.g., workplace, home, entertainment venues) based on observable behavioral patterns rather than manual classification.
2Adaptability or versatility
If users manually set semantic labels for all visited places, then the personalization completeness is improved, but the time consumption increases
Solution Approach 1:
The system performs preliminary analysis of user behavior patterns and pre-calculates place attributes (visit frequency, duration, time patterns) as users visit locations. By continuously monitoring and storing these attributes in advance, the system prepares the necessary data for automatic semantic labeling, eliminating the need for users to spend time manually categorizing locations later.
Solution Approach 2:
The system uses feedback from user behavior patterns to continuously refine and improve semantic labeling accuracy. By analyzing visited places, duration of stay, and temporal patterns, the system adapts to user preferences and automatically adjusts labeling, providing increasingly accurate personalization without additional user time investment.
3Device complexity
If semantic labels are pre-defined by service providers, then the system complexity is reduced, but the personalization accuracy deteriorates
Solution Approach 1:
The system segments the semantic labeling process into distinct functional modules: a place identifier processor that generates place attributes from location data, a group identifier processor that clusters locations based on similar attributes, and a label determiner that assigns semantic labels. This segmentation allows each module to specialize in specific tasks, maintaining system manageability while achieving high personalization accuracy through automated behavior analysis.
Data Source
AI summary
A semantic labeling apparatus and method thereof include a place identifier processor configured to, based on location data of a user, generate place attributes of places that indicate information of a user visit for each place, wherein user location remains unchanged within the places for a predetermined period of time. A group identifier processor is configured to cluster the places based on the place attributes, classify the places into groups, acquire a semantic label for each of the groups, and designate the acquired semantic label as the semantic label of each of the groups. A label determiner is configured to determine the semantic label of each of the groups as a semantic label of each member place of each of the groups.