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Personalized Information Recommendation Based on Network Bookmarks

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 163)

Abstract

In order to solve the current Internet “the information to overload” the question, fast, accurately obtains the valuable information from the network resource, the personalized information recommends as one of powerful tools to arise at the historic moment, This article has profited from the complex network mass organization structure division method, proposed based on the network bookmark’s personalized information recommendation method.s This paper describes the concept and model of web bookmarks, theoretical knowledge of personalized recommendations, and mass organization structure based on network bookmark, and finally introduces the division of a society structure based on CPM algorithm.

Keywords

Network bookmark Mass organization structure Personalized information recommendation K- clique CPM algorithm 

References

  1. 1.
    Zhang Shuren (2006) Web2.0 to the complex adaptation information system research. Renmin University of China computer applied technology specialized doctorate paper 1:28:237–243Google Scholar
  2. 2.
    Pan Mei (2006) User information space from construction—network bookmark. Libr J 6:123–124Google Scholar
  3. 3.
    Zhao Peng, Cai Qingsheng, Wang Qingyi (2008) In the crossing linking network may overlap the mass organization structure analysis algorithm. Huanan Univ Sci Technol J (natural sciences version) 5:19–23Google Scholar
  4. 4.
    Ye Liangyan (2010) Personalization recommendation systemBased on web diary excavation construction. Comput Netw 21:573–575Google Scholar
  5. 5.
    Lu Lina, Yang Yi Ling (2000) In web diary excavation data pretreatment research. Comput Proj 26(4):66–68Google Scholar
  6. 6.
    Palla G et al (2005) Uncovering the overlapping community structure of complex networks in nature and society. Nature 7043:814–818CrossRefGoogle Scholar
  7. 7.
    Tan Jian, Yu Xiaojun (2008) Based on clique filter algorithm enterprise colony modulation applied research. Science progress and decision-making 12:122–124Google Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Information Engineering CollegeNingbo Dahongying UniversityNingboChina

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