Mobile phone user behavior similarity analysis method based on mobile big data

A similarity analysis and mobile phone user technology, applied in the field of mobile big data applications, can solve the problem of individual user data deletion and other issues, and achieve a high degree of business fit

Pending Publication Date: 2019-12-13
北京融信数联科技有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The traditional algorithms for studying the similarity of user behavior trajectories using the signaling of mobile operators are all based on density clustering, which can easily discover and grasp the commonality of group user activities, but for some individual user data, it is easy to be regarded as Noise points are removed

Method used

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  • Mobile phone user behavior similarity analysis method based on mobile big data
  • Mobile phone user behavior similarity analysis method based on mobile big data
  • Mobile phone user behavior similarity analysis method based on mobile big data

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Embodiment Construction

[0017] Realization of the present invention is based on following theory:

[0018] 1. PF-IGF (Person Frequency-Inverse Group Frequency) theory.

[0019] PF (Person Frequency) means the length of stay or visit frequency of a specific person (mobile phone user) in a certain time-space location, and GF (Group Frequency) refers to the length of stay or visit frequency of a certain group (a group of mobile phone users) in a corresponding time-space location. The average value of access frequency, while IGF (Inverse Group Frequency) is the inverse of GF, PF-IGF is to use GF as the denominator and PF as the numerator to perform joint calculations. PF-IGF expects to prominently reflect that a certain user frequently visits a specific spatio-temporal location, while other users in the group are not so keen on this area. In other words, if a person's PF-IGF is high in a certain location, it means that the location can "represent" or "characterize" the user's behavior track characterist...

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Abstract

The invention provides a mobile phone user behavior similarity analysis method based on mobile big data, and the method comprises the steps: collecting the movement track information of a user group in a time period through the signaling data of a telecom operator; counting the occurrence frequency or residence time length PF of each user in each sector in the time period and the mean value GF ofthe occurrence frequency or residence time length of all the users in each sector, and calculating PF / GF to obtain a PF-IGF value of each user; calculating an included angle cosine value of the PF-IGFvalues of every two mobile phone users, and judging the behavior similarity of the two users according to the obtained included angle cosine value: the larger the included angle cosine value theta is, the closer of the activity behavior tracks of the two mobile phone users are; wherein the smaller the value of the included angle cosine value is, the more irrelevant of the activity behavior tracksof the two mobile phone users are. The method can be used for researching the similarity degree between every two persons and finding the similar crowd of each person in a specified range.

Description

technical field [0001] The invention belongs to the technical field of mobile big data applications, and in particular relates to a mobile phone user behavior similarity analysis method based on mobile big data. Background technique [0002] With the popularization of smart phones, the development of mobile big data technology has been promoted. Mobile big data contains a wealth of information, each user has a special movement radius, and has a very high probability of traveling to and from important places. By mining it, we can discover the behavior patterns of similar users, and the behavior patterns usually reflect the user's identity and habits. Through the identification of similar groups of people, the degree of closeness between different users can be discovered, and guidance can be provided for precision marketing and intelligent recommendation. The traditional algorithms for studying the similarity of user behavior trajectories using the signaling of mobile operat...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04W8/18H04W8/20G06N20/00G06Q30/02
CPCH04W8/183H04W8/205G06Q30/0201
Inventor 张广志成立立刘增礼秦星星
Owner 北京融信数联科技有限公司
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