Social network user dynamic and static interest mining method

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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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 the calculation of similarity of microblog media users When the degree is high, the user background and generated content are considered, but the generated content does not take into account the distribution of topics at different time points

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