A Location Prediction Method Based on User Movement Pattern

Abstract

The invention relates to a location prediction method based on user movement patterns, which belongs to the field of machine learning. The method is as follows: use the Apriori algorithm to mine the individual movement patterns of each user, and find out the internal factors that affect the user's sign-in; use the dynamic time warping algorithm DTW to calculate the similarity between the individual movement patterns of the users; cluster the individual movement patterns of the users Group the modes to get the central mode of each group and find the external factors that affect the sign-in; use the individual mobile mode and the overall mobile mode to train the Markov model; train the Markov chain model based on IMP and AMP to predict the user's The next location; consider the influence of external weather, create a general weather feature; use the Gaussian kernel function to calculate the similarity between the weather at the current location and the weather at other locations, and correct the predicted results; set evaluation standards and benchmark methods. The invention makes the predicted result more suitable for real life.

Description

technical field [0001] The invention belongs to the field of machine learning and relates to a position prediction method based on user movement patterns. Background technique [0002] With the popularization of mobile terminals, it is easier to obtain human mobility data. Location-based social networking platforms have also collected a large amount of user check-in data. Research on human mobility patterns has become a hot topic, and it has become possible to study people's mobility patterns. Among them, location prediction is more common. Through location prediction, users' mobile preferences can be known in advance, and the mobile tendency of people can also be understood, which can not only provide targeted services to users, but also bring benefits to businesses. Existing research mainly analyzes the user's behavior through the user's check-in history, finds the user's movement rule, and then predicts the location. Among them, most of the factors considered are time, ...

AI Technical Summary

Problems solved by technology

In addition, most studies are based on the prediction of the user's personal mobility patterns. If the user goes to a place that has never been there, there is no data available to train the model; some researchers also use the overall check-in data to train the model, so that the model It can be applied to the location prediction of all users. However, the prediction based on the overall data is too coarse-grained. If the user is currently in the same place, the final prediction result is the same place, which is inconsistent with the actual situation.

[0003] Aiming at the problem that the traditional position prediction model based on discrete state sequences cannot predict the position well, the present invention considers the correlation between different positions in the user check-in trajectory, and excavates the individual user's movement pattern and the overall position from the user historical check-in data. mobile mode, that is, the internal and external factors that affect user sign-in

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