Location-based service travel recommendation method

Abstract

The invention provides a location-based service travel recommendation method comprising the steps that step one, entity track-event information is acquired, and user-location-activity associated modeling is performed on the entity information through a three-dimensional tensor; step two, user track-event information is recorded; and step three, similarity matching is performed through user analogy, and the user track-event information with high similarity is actively pushed, wherein the user track-event information (from location information of mobile phones, sports bracelets and sports watches) is integrated and then matched with the related user track-event information, and pushing is performed. According to the method, mass user data are integrated and similar users and activities are found in different locations by using a collaborative filtering algorithm, modeling of the mutual relation of user-location-activity is performed through the tensor and a regularized tensor / matrix decomposition algorithm is put forward. The problem of sparsity of single user movement location data can be effectively alleviated by the algorithm and thus mining of the movement mode of the users can be performed.

Description

technical field [0001] The present invention relates to the application of cloud computing and big data, in particular to the location modeling of individual personnel information and the method of integrating existing low-dimensional information into new modeling weighting application, especially to the travel recommendation method based on location services. Background technique [0002] First of all, in the existing big data environment, there are countless software and devices for recording location based on mobile terminals. Device products with geographic location information tracking functions include mobile phones, sports bracelets, sports watches, etc. These existing devices with a wide market and a large number of users have accumulated a large amount of geographic location data. [0003] Secondly, the existing data push or customer data acquisition is mostly based on the intentional push based on the address left by the initiating terminal's first visit, but canno...

AI Technical Summary

Problems solved by technology

[0003] Secondly, the existing data push or customer data acquisition is mostly based on the intentional push based on the address left by the initiating terminal’s first visit, but cannot meet the push of similar needs, habitual needs, and analogous needs

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