Recommendation method based on improved pagerank and comprehensive influence

A recommended method and influential technology, applied in data processing applications, instruments, calculations, etc., can solve the problems of insufficient refinement and scientificity of indicators

Active Publication Date: 2022-02-15
HANGZHOU DIANZI UNIV
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AI Technical Summary

Problems solved by technology

Traditional PageRank lacks this part. Existing previous studies have proposed the idea of ​​adding influence indicators. Some researchers directly select specific big V users for research, which is obviously a bit too general; some researchers set several measures Indicators, but the indicators are not detailed and scientific enough, so the impact indicators need to be evaluated more comprehensively

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  • Recommendation method based on improved pagerank and comprehensive influence
  • Recommendation method based on improved pagerank and comprehensive influence
  • Recommendation method based on improved pagerank and comprehensive influence

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Embodiment Construction

[0046] The user recommendation method based on the improved version of the directed graph PageRank and the comprehensive assessment of microblog influence provided by the present invention is mainly divided into four steps:

[0047] (1) The Python crawler crawls the Sina Weibo dataset; (2) The directed graph model is established on the basis of the attention relationship and interaction relationship through the dataset; (3) The weighted probability transfer matrix is ​​obtained by adding the comprehensive index evaluation matrix; ( 4) Markov iterative convergence to obtain the final PR value and perform Top-N recommendation.

[0048] (1) Python crawler crawls Sina Weibo dataset

[0049] The Python crawler crawls about 2,000 users on Sina Weibo and the hundreds of thousands of fans derived from it, as well as the following user data set. This module needs to crawl four data tables: ①User_info table (user information table) ②Follows table (user attention table )③Followers (fan ...

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Abstract

The invention discloses a user recommendation method based on the improved version of PageRank of the directed graph and the comprehensive evaluation of Weibo influence, which includes the following steps: (1) Python crawler crawls the Sina Weibo data set; Establish a directed graph model on the basis of relationships and interactions; (3) Add a comprehensive index evaluation matrix to obtain a weighted probability transition matrix; (4) Markov iterative convergence to obtain the final PR value and make Top‑N recommendations. Compared with the traditional method, the method of the present invention can effectively avoid the limitations and inaccuracies caused by only focusing on relationship modeling, or the evaluation index is too single, and achieve more accuracy and higher reliability in the data range The determination of influential users.

Description

technical field [0001] The invention belongs to the technical field of social media user recommendation, and in particular relates to a user recommendation method based on an improved version of PageRank of a directed graph and a comprehensive evaluation of microblog influence. Background technique [0002] Microblog has become the most popular mass information dissemination medium nowadays, users can easily complete various information acquisition, production, sharing and dissemination through the microblog platform. Another great charm of Weibo is that you can meet more friends in this virtual network. Taking Sina Weibo as an example, users can find like-minded users through the system-recommended users in the "People You May Be Interested in" module. people. But in fact, the existing Weibo does not recommend users who really have influence on Weibo. Most of the recommended users are salesmen or micro-businesses, and people are less likely to really start paying attention...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F16/9535G06Q50/00
CPCG06Q50/01
Inventor 黄彬彬何馥芸沈艳婷杨泽彬
Owner HANGZHOU DIANZI UNIV
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