Local trajectory-based interest point recommendation method

A recommendation method and point-of-interest technology, applied in the field of point-of-interest recommendation based on local trajectories, can solve the problems that the difference cannot be reflected, and the frequency of user check-in is ignored

Inactive Publication Date: 2018-02-16
HUNAN UNIV
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  • Summary
  • Abstract
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  • Application Information

AI Technical Summary

Problems solved by technology

[0012] 2. Ignoring the user's check-in frequency
In the...

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  • Local trajectory-based interest point recommendation method
  • Local trajectory-based interest point recommendation method
  • Local trajectory-based interest point recommendation method

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Embodiment 1

[0042] In order to predict the user's movement trajectory more accurately, we propose the local trajectory movement model LTMM, which uses the power law distribution to model the user's movement probability for different location trajectory distances in the local activity area.

[0043] In order to describe the specific steps of the LTMM algorithm, we first introduce some concepts related to this patent model.

[0044] 1. Track: A user's track is the GPS track of two check-in locations generated according to the access time. Such as figure 2 As shown, in a two-dimensional space, we can convert the user's check-in location into a GPS track according to the check-in time. Each location point pi contains latitude, longitude, and timestamp. figure 2 All trajectory sets of users in T={p 1 →p 2 ,p 2 →p 3 ,p 3 →p 4 ,p 4 →p 5 ,p 5 →p 6 ,p 6 →p 7 ,p 7 →p 8 ,p8 →p 9 ,p 9 →p 10 ,p 10 →p 11 ,p 11 →p 12 ,p 13 →p 14 ,p 14 →p 15 ,p 15 →p 16 ,p 16 →p 17}, a to...

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Abstract

The invention discloses a local trajectory-based interest point recommendation method. The method comprises the following steps of: 1, finding out a center position of a user; 2, calculating a local movement area of the user according to the center position; 3, carrying out statistic on movement trajectories of the user in the local movement area; 4, calculating movement probabilities of the userat different distances; and 5, calculating sign-in probabilities of positions which are not accessed by the user in the local movement area, sorting candidate positions according to the sign-in probabilities and returning c candidate positions with the maximum probabilities to the user as recommendation results, wherein c is a set threshold value. Compared with the traditional collaborative filter-based POI recommendation method and geographical influence-based Naive Bayes recommendation method, the method disclosed by the invention is capable of improving the accuracy and recall rate of POI recommendation, helping position-based social network sites to enhance the user experience and increasing the revenue.

Description

technical field [0001] The invention belongs to the field of electronics, and in particular relates to a method for recommending interest points based on local trajectories. Background technique [0002] With the popularization of functions such as location check-in, location sharing, and location identification in many mobile social networks, location-based social networks (LBSNs) have attracted more and more users, such as Foursquare, Gowalla, and Facebookplace. Recommending attractive locations (POIs) to users has also become popular. POI recommendations not only help users explore new locations to enrich their experience, but also help mobile social networking sites increase revenue. [0003] In recent years, many universities and research institutions have carried out in-depth research on POI recommendation. Among them, exploring the geographical characteristics of check-in data from the perspective of users is an aspect that cannot be ignored. A better method is to ...

Claims

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Application Information

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IPC IPC(8): G06F17/30
CPCG06F16/9537
Inventor 姜文君史杨凯
Owner HUNAN UNIV
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