User attribute information acquiring method and device

Abstract

The invention discloses a user attribute information acquiring method and device. An embodiment of the method comprises steps as follows: acquiring track data which comprise time information and position information of multiple track points; pre-processing the track data to acquire to-be-processed track data; analyzing correlation of the to-be-processed track data to extract a correlated track data set of the to-be-processed track data; calculating similarity between the to-be-processed track data and the correlated track data set; determining user attribute information corresponding to the to-be-processed track data according to the similarity. With the adoption of the method and the device, the accuracy of the user attribute information acquired on the basis of the track data is improved.

Description

technical field [0001] The present application relates to the field of computer technology, specifically to the field of terminal technology, and in particular to a method and device for acquiring user attribute information. Background technique [0002] With the development of mobile Internet technology and the popularization of location service applications, a large amount of user trajectory data has been generated. Since the user trajectory data has a close relationship with the user's attribute information, the movement characteristics between different users are relatively independent, and the movement characteristics between users with social relations have a certain correlation, so the trajectory data can be used To analyze the user's personal attribute information and social attribute information. [0003] At present, a variety of methods for analyzing user attribute information based on trajectory data have been proposed. The similarity between track data can be a...

AI Technical Summary

Problems solved by technology

These methods usually have higher requirements on the quality of trajectory data, and the accuracy of analysis results for sparse or irregular trajectory data is poor.

In large-scale trajectory data analysis, due to the large differences in the data density, adoption frequency, and time distribution of trajectory data, and the trajectory data is obtained based on different electronic devices or different user IDs, the current trajectory feature extraction methods cannot target large-scale trajectory data. Effective and robust features are extracted from the data for similarity analysis, so it is impossible to accurately estimate user attribute information

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