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Content Provider | IEEE Xplore Digital Library |
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Author | Zhang Ji-Lin Ren Yong-jian Zhang Wei Xu Xiang-Hua Wan Jian Weng Yu |
Copyright Year | 2010 |
Description | Author affiliation: School of Computer, Hangzhou Dianzi Unviersity, Hangzhou, 310018, China (Zhang Ji-Lin; Ren Yong-jian; Zhang Wei; Xu Xiang-Hua; Wan Jian) || School of Information Engineering, University of Science and Technology Beijing, 100083, China (Weng Yu) |
Abstract | This paper analyzes the development of the PageRank algorithm both domestic and international, expounds the practical application and development of the algorithm. On this basis, the dissertation analyzes the thinking of the traditional PageRank algorithm, sums up the advantages and disadvantages, and introduces several important PageRank improved algorithms and their thinking. The paper provides a time-feedback PageRank algorithm improvement for the shortcoming of the traditional PageRank algorithm focus on the old websites. Adding a feedback factor in the traditional PageRank algorithm can accelerate the old and useless Webs sinking, the new Webs rising, and taking into account the old and important webs not sink. Implementing the PageRank value is plus or minus with the time of webs. On the basis of theoretical research, implements the rank model based on the PageRank algorithm, the model contains the traditional web ranking system model and improved web ranking system model. Experiment on this model, contrasting the experimental results. Experimental results show that the improved algorithm can improve the satisfaction of the user's search results. |
Starting Page | 4811 |
Ending Page | 4814 |
File Size | 550335 |
Page Count | 4 |
File Format | |
ISBN | 9781424476169 |
e-ISBN | 9781424476183 |
DOI | 10.1109/ICISE.2010.5691573 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2010-12-04 |
Publisher Place | China |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Computers Google Analytical models Ranking Algorithm Search engines Search Engine PageRank Educational institutions Time Feedbacking Artificial intelligence Web search |
Content Type | Text |
Resource Type | Article |
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