Advertisement

The Blog Ranking Algorithm Using Analysis of Both Blog Influence and Characteristics of Blog Posts

  • Jiwon KimEmail author
  • Unil Yun
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 274)

Abstract

In recent years, while amounts of the information in the blogosphere increase rapidly, the problems of information quality have come up. Discovering a good quality data is the important issue in blog space with overflowed information. In this paper, we present WCT algorithm for efficient blog ranking. This method performs a ranking process using both interconnection of blogs and structural weights for content in blog. In the performance analysis, we discuss the comparison between our algorithm and the previous algorithm for blog ranking. The result shows that our proposal has the high performance than other blog retrieval method.

Keywords

Blog ranking Information retrieval Blog structure 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Brin, S., Page, L.: The Anatomy of a Large-scale Hypertextual Web Search Engine. In: Proceedings of 7th International World Wide Web Conference, Computer Networks and ISDN Systems, vol. 30(1-7), pp. 107–117 (1998)Google Scholar
  2. 2.
    Fautsch, C., Savoy, J.: Adapting the Tf-Idf Vector-space Model to Domain specific Information Retrieval. In: SAC 2010, pp. 1708–1712 (2010)Google Scholar
  3. 3.
    Jarvelin, K., Kekalainen, J.: IR evaluation methods for retrieving highly relevant documents. In: SIGIR 2000: Proceedings of the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 41–48 (2000)Google Scholar
  4. 4.
    Kim, l.H., Yoon, T.B., Kim, K.S., Lee, J.-H.: The Trackback-Rank Algorithm for the Blog Search. In: IEEE International Multi-topic Conference 2008, pp. 454–459 (2008)Google Scholar
  5. 5.
    Kleinberg, J.M.: Authoritative sources in hyperlinked environment. Journal of the ACM 46(5), 604–632 (1999)MathSciNetzbMATHCrossRefGoogle Scholar
  6. 6.
    Lee, J.: A Study on the Pivoted Inverse Document Frequency Weighting Method. Journal of the Korea Society for Information Management 20(4), 233–248 (2003)CrossRefGoogle Scholar
  7. 7.
    Momma, M., Chi, Y., Lin, Y., Zhu, S., Yang, T.: Influence Analysis in the Blogosphere. The Computing Research Repository, CoRR 2012, abs/1212.5863 (2012)Google Scholar
  8. 8.
    Santos, R.L.T., Macdonald, C., McCreadie, R.M.C., Ounis, I., Soboroff, I.: Information Retrieval on the Blogosphere. Foundations and Trends in Information Retrieval 6(1), 1–125 (2012)zbMATHCrossRefGoogle Scholar
  9. 9.
    Yeung, C.M.A., Noll, M.G., Gibbins, N., Meinel, C., Shadbolt, N.: SPEAR: Spamming-Resistant Expertise Analysis and Ranking in Collaborative Tagging Systems. Computational Intelligence 27(3), 458–488 (2011)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Department of Computer EngineeringSejong UniversitySeoulKorea

Personalised recommendations