Dynamic POIs recommendation method based on TS24

A recommendation method and dynamic technology, applied in neural learning methods, biological neural network models, instruments, etc., can solve problems such as insufficient to meet user needs

Active Publication Date: 2021-05-11
DONGBEI UNIVERSITY OF FINANCE AND ECONOMICS
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AI Technical Summary

Problems solved by technology

In the past few years, some social networks have failed, such as Brightkite, Gowalla, and Jiepang, and the main reason for failure is that the quality of personalized Point-of-Interests (POIs) recommendation service is not enough to meet user needs [1]

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  • Dynamic POIs recommendation method based on TS24
  • Dynamic POIs recommendation method based on TS24
  • Dynamic POIs recommendation method based on TS24

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specific Embodiment approach

[0033] 1. A dynamic POIs recommendation method based on TS24, including the following steps,

[0034] Step 1: Build a dynamic POIs (Points of Interest) recommendation framework based on 24 time periods. for the current time period The T-SemiDAE POIs recommendation model above is implemented by the second to fifth steps below.

[0035] Step 2: Construction The sample set Θ on cur . It is known that the user set in the LBSN dataset is is the total number of users. The collection of POIs is is the total number of POIs. user The checked-in POIs constitute a set of and yes The number of POIs in . Use the Term Frequency-Inverse Document Frequency (TF-IDF) technology to convert the number of user check-ins into the number of times the user has checked in The preference value of [6]:

[0036]

[0037] in is a user right the number of check-ins, is the POI category to users The influence weight of [1]:

[0038]

[0039] in means user In...

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Abstract

The invention discloses a dynamic POIs (Point of Interest) recommendation method based on TS24, and belongs to the technical field of computer application. The method comprises the following steps: 1, building a dynamic POIs recommendation architecture based on 24 time periods; 2, converting the sign-in frequency of a user into a preference value of the user for the sign-in POIs of the user by using a TF-IDF technology, and constructing a sample set theta cur in a current time period; 3, establishing a SemiDAE geographical influence model on the Tau cur; 4, according to the similarity of the sign-in behaviors of the users in the similar time periods, establishing a T-SemiDAEPOIs recommendation model with the time influence on the Tau cur; and 5, according to the two steps of pre-training and parameter fine tuning, training the T-SemiDAEPOIs recommendation model on the Tau cur. According to the method, the dynamic POIs recommendation model based on the deep learning technology is built in a novel and more reasonable mode, geographical and time information of the location social network is mined and fused, and experimental results show that the applied technology can remarkably improve the precision and recall rate of personalized POIs recommendation.

Description

technical field [0001] The invention belongs to the technical field of computer applications and relates to a TS24-based dynamic POIs recommendation method. Background technique [0002] The present invention takes Location-Based Social Networks (LBSNs) as the main research object. In the past few years, some social networks have failed, such as Brightkite, Gowalla, and Jiepang, and the main reason for failure is that the quality of personalized Point-of-Interests (POIs) recommendation service is not enough to meet user needs [1] . Personalized POIs recommendation can greatly improve the quality of location-based social network services and benefit both users and POIs owners. For example, users can find POIs they might like. POIs providers may also use targeted advertising or promotions for specific users. These recommendations not only enhance the stickiness of users, but also increase the revenue of POIs providers [1] . To some extent, the quality of POIs recommendat...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/9535G06F16/9536G06F16/9537G06N3/08G06Q50/00
CPCG06F16/9535G06F16/9536G06F16/9537G06Q50/01G06N3/08
Inventor 王晓军刘涛
Owner DONGBEI UNIVERSITY OF FINANCE AND ECONOMICS
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