Social network user dynamic and static interest mining method

A social network, user technology, applied in data processing applications, instruments, computing, etc., can solve problems such as not taking into account the distribution of topics

Active Publication Date: 2016-11-09
连云港钻尚网络科技有限公司
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Problems solved by technology

[0006] In the journal published in China in 2016: Journal of Computer Science, the title is: Similar User Discovery Method for Specific Users in Microblog, the authors are: Zhong Zhaoman, Hu Yun, Li Cunhua, Liu Zongtian, this article is in t

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  • Social network user dynamic and static interest mining method
  • Social network user dynamic and static interest mining method
  • Social network user dynamic and static interest mining method

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[0030] The implementation process of the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0031] refer to figure 1 , the social network user dynamic and static interest mining method of the present invention, the steps are as follows:

[0032] Step 102, extract the static interest SI={SI from the background information profile of the user user 1 , SI 2 ,...,SI m}, each interest point SI i is a two-tuple SI i =(kw i ,w i ), 1≤i≤m, where kw i as the keyword, w i For user to kw i preference weight, refer to figure 2 , the specific steps are as follows:

[0033] Step 201, when the user defines his own tags on the social network, he can either select from the tag library or input them manually. Tags can be understood as meaningful strings, and the collected user tags are formed into a library. During word segmentation, they are recognized as a whole. The tag of the user user is recorde...

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Abstract

The invention discloses a social network user dynamic and static interest mining method comprising the following steps that background information profile and generation content of a user are acquired from social media; the static interests SI={SI1, SI2,...,SIm} are extracted from the background information profile of the user, each interest point SIi is a binary group SIi=(kwi, wi), and 1<=i<=m, wherein the kwi is the keyword, and wi is the preference weight of the user for kwi; and dynamic DI={DI1, DI2,...,DIn} is extracted from the generation content of the user, each interest point is a triple group DIi=(topici,wi,T), and 1<=i<=n, wherein topici is formed by multiple keywords, wi is the preference weight of the user for topici, and T={t1,t2,...,ts}, ti (1<=i<=s) is each time point of discussing topici of the user, i.e. ti (1<=i<=s) refers to the distribution situation at different time points. According to the method, the characteristics of the interests of the social media users can be more reasonably described so that the method is more suitable for subsequent deep analysis of the characteristics of the interests of the social media users.

Description

technical field [0001] The invention relates to an information mining technology, in particular to a method for mining dynamic and static interests of social media users. Background technique [0002] Personalized recommendation in social networks, discovery of domain experts, and community division are current research hotspots in social computing, and effectively mining the interests of social media users is the basic work of this type of research. However, the existing research failed to distinguish the dynamic and static characteristics of social network user interests, using a unified user interest representation model, user interest UI={Int 1 , Int 2 ,...,Int m}, each interest point is a two-tuple Int i =(topic i ,w i ), topic i is a topic, usually composed of multiple keywords; w i For users to topic i preference weight. [0003] Proceedings of the conference published in the United States in 2011: Proceedings of 2011 Visual Information Communication-Internat...

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Application Information

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IPC IPC(8): G06Q50/00
CPCG06Q50/01
Inventor 仲兆满管燕
Owner 连云港钻尚网络科技有限公司
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