POI Tag Optimization via Ternary Self-Adaptive Collaborative Learning
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Solution Overview
Problem
Current methods for acquiring and optimizing tags of points of interest in map applications, such as manual annotation and natural language processing, face challenges in accuracy and completeness, leading to incorrect tags and under-tagging of points of interest.
Innovation Solution
A method and apparatus that generate a point of interest-tag matrix, extract features from this matrix, and input them into a pre-trained ternary self-adaptive collaborative learning model to optimize tags by calculating scores, thereby improving the matching degree between points of interest and their tags.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation or natural language processing is used to acquire tags, then the process is simple, but the accuracy and completeness of tags deteriorate
Solution Approach 1:
The patent segments the tag optimization process into multiple independent modules: a point of interest representation module that extracts features from POI data, a tag representation module that encodes tag information, a scoring module that calculates matching scores, and an optimization module that updates tags. This segmentation allows each module to be optimized independently while maintaining overall system accuracy without excessive complexity
Solution Approach 2:
The patent introduces a scoring mechanism as an intermediary between the POI representation and tag optimization. The scoring module calculates matching scores between POI features and tag embeddings, serving as a mediator that quantifies the relationship and enables precise tag optimization without requiring complex direct optimization algorithms
2Measurement precision
If manual annotation is used to acquire tags, then the process is straightforward, but the completeness of tags deteriorates
Solution Approach 1:
The patent implements a feedback mechanism where the tag optimization module uses the calculated matching scores to update tag embeddings, and these updated embeddings are fed back into the scoring process. This iterative feedback loop continuously improves tag completeness by identifying and adding relevant tags that were previously missed, while the automation of this process keeps complexity manageable
Solution Approach 2:
The patent performs preliminary feature extraction and encoding before the actual tag optimization. The point of interest representation module pre-processes POI data into meaningful features, and the tag representation module pre-encodes tag information into embeddings. This preliminary action prepares the data in advance, enabling more complete and accurate tag optimization without increasing real-time processing complexity
3Extent of automation
If natural language processing is used to extract tags, then automation is improved, but the accuracy of tags deteriorates
Solution Approach 1:
The patent replaces traditional natural language processing text-based tag extraction with a machine learning-based embedding approach. Instead of relying on NLP to parse and extract tags from text, the system uses trained neural network models to generate tag embeddings that capture semantic relationships, significantly improving tag accuracy while maintaining automation. The scoring module then uses these embeddings to precisely match POIs with appropriate tags
4Reliability
If traditional tag acquisition methods are used, then the process is simple, but the matching degree between points of interest and tags deteriorates
Solution Approach 1:
The patent changes the fundamental parameters of tag representation from discrete text labels to continuous vector embeddings. By representing both POIs and tags as vectors in the same semantic space, the system enables precise matching through vector similarity calculations. The scoring module computes matching scores based on these vector representations, dramatically improving POI-tag matching quality while the modular architecture keeps the complexity of handling high-dimensional vectors manageable
Data Source
AI summary
Embodiments of the present disclosure provide a method and apparatus for optimizing a tag of a point of interest. The method includes: acquiring a set of points of interest and a set of tags of points of interest in the set of points of interest; generating a point of interest-tag matrix based on the set of points of interest and the set of tags of points of interest in the set of points of interest; extracting a feature of a point of interest-tag in the point of interest-tag matrix; inputting the feature of the point of interest-tag in the point of interest-tag matrix into a pre-trained ternary self-adaptive collaborative learning model, to obtain a point of interest-tag score matrix; and optimizing the set of tags of points of interest in the set of points of interest based on the point of interest-tag score matrix.