Tensor decomposition model-based interest point type prediction method

Abstract

The invention discloses a tensor decomposition model-based interest point type prediction method. The method includes the following steps that: a) the access point coordinate information and stay timeof users in T time periods in each day are obtained from new energy vehicle trajectory data; b) the ranges of the stay areas of the users are defined as uncertain areas according to time periods whenthe users are located at access points, the durations of the stay of the users, the IDs of the types of interest points in the uncertain areas are obtained, and tensors are constructed; c) clusteringprocessing is performed on trajectory data corresponding to all the users; d) the types of the interest points of the users are predicted by using a tensor decomposition model; e) after the information of work, places of residence and places of departure is combined, the similarity of the types of interest points between the places of departure and destinations are adopted as the regularization item constraint of tensor decomposition, and the low-rank approximate solutions of the tensors are calculated; and f) an objective function is iteratively optimized by using an alternating least squares method, and the probabilities of the access of different kinds of interest points by the users are obtained and are adopted as a prediction results. With the method of the invention adopted, the types of the interest points accessed by the users can be accurately predicted.

Description

technical field [0001] The invention belongs to the field of mobile behavior analysis, and more specifically relates to a method for predicting the types of interest points based on a tensor decomposition model. Background technique [0002] With the development of positioning technology and mobile computing, more and more trajectory data can be collected and applied to various researches, which has become an increasingly important research topic. These trajectory data reflect the movement laws, behavior preferences and even interest preferences of various moving objects such as crowds and vehicles. At the same time, research on user interest points has also received extensive attention from industry and academia. [0003] Among them, how to identify and predict the types of interest points visited by mobile users from trajectory data is an important application in mobile behavior analysis. Because the location reported by the mobile device (such as: mobile phone base stat...

AI Technical Summary

Problems solved by technology

The method of using auxiliary information as a regularization term is less used in the field of mobile data prediction

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