Path Representation Model Training via Trajectory Masking
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Solution Overview
Problem
Current path representation models in big data analysis lack efficiency and semantic representation, particularly in transforming user trajectories into effective vectors for prediction tasks, such as next movement prediction and identity estimation.
Innovation Solution
A method and apparatus for training a path representation model by acquiring user trajectory points, inputting them into a pre-trained model, and adjusting network parameters based on differences between passed places and trajectory positions, using self-supervised training and masking techniques to enhance modeling efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional path representation models are used to transform user trajectories into vectors, then the basic transformation function is achieved, but modeling efficiency and semantic representation are insufficient
Solution Approach 1:
The patent applies preliminary action by pre-training a language model on trajectory data before fine-tuning for specific tasks. The pre-training phase pre-processes and extracts semantic features from raw trajectory points, creating a foundation that improves both semantic representation and modeling efficiency in subsequent tasks
Solution Approach 2:
The patent changes parameters by adjusting network architecture configurations, learning rates, and training hyperparameters during the fine-tuning process. These parameter optimizations enable the model to achieve high semantic representation accuracy while maintaining computational efficiency
2Measurement precision
If complex training methods are used to improve model accuracy, then prediction accuracy improves, but computational requirements increase
Solution Approach 1:
By performing pre-training on large-scale trajectory data before task-specific fine-tuning, the model learns generalizable features in advance. This preliminary action reduces the computational burden during actual prediction tasks, as the model has already captured essential patterns
Solution Approach 2:
The model performs self-supervised learning by automatically generating training signals from the trajectory data itself without requiring extensive manual annotation. This self-service mechanism reduces computational requirements for data preparation and model training
Data Source
AI summary
A method and an apparatus for training a path representation model are provided. The method may include: acquiring at least one trajectory point of at least one user, where each trajectory point of each user includes a place passed by the user, a start time and a duration; inputting the at least one trajectory point of the at least one user into a pre-trained model to obtain a trajectory representation of each user; obtaining, for each user, a position of each trajectory point from the trajectory representation of the user by searching according to the start time and the duration of each trajectory point of the user; and adjusting a network parameter of the pre-trained model according to a difference between the place passed by each user and the position of each trajectory point obtained by searching, to obtain a path representation model.