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Article deliverer recommending method based on user mobility prediction

A recommendation method and user mobility technology, applied in data processing applications, commerce, instruments, etc., can solve problems such as ineffective combination of time factors

Inactive Publication Date: 2015-08-26
FUZHOU UNIV
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  • Abstract
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  • Application Information

AI Technical Summary

Problems solved by technology

[0004] Among them, the method of mobile prediction is needed, but most of the mobile prediction algorithms currently studied only consider the factor of space to predict the next location of the user, and cannot effectively combine the time factor

Method used

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  • Article deliverer recommending method based on user mobility prediction
  • Article deliverer recommending method based on user mobility prediction
  • Article deliverer recommending method based on user mobility prediction

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Embodiment Construction

[0047] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0048] Please refer to figure 2 , the present invention provides a method for recommending item deliverers based on user movement prediction, which is characterized in that it includes the following steps:

[0049] Step S1: Raw GPS point g i The preprocessing of , including the extraction of location p and the clustering of location s;

[0050] In this embodiment, many original GPS points are meaningless, such as the GPS data on the road. In order to make the prediction more meaningful, it is necessary to extract a meaningful location p. The specific method of extracting the location p is as follows:

[0051] Raw GPS point g i contains longitude g i .Lgt, latitude g i .Lat and record g i .T time, ie g i =(g i .Lgt,g i .Lat, g i .T), the user’s original trajectory Traj raw From several consecutive GPS points g i Composition, i.e. Traj raw =...

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Abstract

The invention relates to an article deliverer recommending method based on user mobility prediction. The method comprises the following steps: 1) data acquisition and arrangement: extracting effective sites from original GPS data of a mobile user and carrying out cluster on the sites to obtain user-interested sites and meanwhile, generating corresponding tracks; 2) candidate selection: extracting a candidate set of candidates according to relationship degree among users in a social network; 3) user meeting prediction: calculating meeting probability between the user and friends thereof through probability statistics of user historical tracks; 4) user position prediction: calculating the probability that the friends of the user visit some place through the probability statistics of the user historical tracks; and 5) deliverer recommendation: calculating the probability that the friends of the user visit some place before the user meets the friends, ranking the probability in a descending order, and selecting and displaying the top K friends. The method provides convenience for user shopping.

Description

technical field [0001] The invention relates to a method for recommending item deliverers based on user movement prediction. Background technique [0002] With the rapid development of mobile terminal equipment, network technology and sensors, mobile terminal equipment such as smartphones integrate sensors with different functions. Using sensors in mobile terminal devices to provide human-centered perception and computing has become a development trend in today's society. In this context, crowd-sensing computing is proposed, which uses the mobile devices of ordinary users as the basic sensing unit to carry out conscious or unconscious collaboration through the mobile Internet to realize the distribution of sensing tasks and the collection and processing of sensing data to complete large-scale, complex social perception tasks. Compared with traditional static sensing-based facilities, crowd-sensing computing has the advantage that a large number of people in society can wor...

Claims

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Application Information

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IPC IPC(8): G06Q30/00
Inventor 於志勇郭文忠郑相涵瞿宜楚
Owner FUZHOU UNIV
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