Advertisement

Personalizing News Documents Using Modified Page Rank Algorithms

  • S. Akhilan
  • S. R. Balasundaram
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 243)

Abstract

World Wide Web is a global village and a rich source of information. The number of users accessing Websites is increasing day by day. For effective and efficient handling, personalization coupled with recommendation techniques provides personalized contents at the disposal of users. News personalization is an area of Web personalization that deals with the extraction of interesting news documents from various news service providers. While surfing the Websites, user interactions with Websites are recorded in Web usage file. These Web log information are useful for constructing user profile which in turn acts as a rich source for news personalization. Since the growth of World Wide Web has resulted in a large amount of data that is now in general freely available for user access, the different types of data have to be managed and organized such that they can be accessed by different users efficiently. Therefore, the application of page rank techniques on the Web is now the focus of an increasing number of researchers. Several page ranking algorithms are used to re-rank the Web pages in the Web. However, these algorithms have to be modified such that they better suit the demands of the user. In this paper, we have proposed modified topic-sensitive and trust-based page rank algorithms for better ordering of news documents to the users.

Keywords

User profile Filtering algorithms Recommendation News personalization Topic-sensitive algorithms Trust-based algorithms 

References

  1. 1.
    Thurman, N., Schifferes, S.: The Future of Personalization at News Websites. Published in Journalism Studies, Taylor & Francis (2012)Google Scholar
  2. 2.
    Li, L., et. al.: Personalized news recommendation: a review and experimental investigation. J. Comput. Sci. Technol. Elsevier Publications (2011)Google Scholar
  3. 3.
    Sokol, M.: Quick Detection of Top-k Personalized Page Rank Lists, vol. 6732, pp. 50–61. Springer-LNCS (2011)Google Scholar
  4. 4.
    Bennett, P., Svore, K., Dumais, S.: Classification-enhanced ranking. In: WWW `10, pp. 111–120 (2010)Google Scholar
  5. 5.
    Chung, F., Zhao, W.: A Sharp Page Rank Algorithm with Applications to Edge Ranking for news personalization, vol. 6516, pp. 2–14. Springer-LNCS (2010)Google Scholar
  6. 6.
    Sakkopoulos, E., et al.; A Web personalizing technique using adaptive data structures: the case of bursts in Web visits. J. Syst. Softw. Elsevier Publications (2010)Google Scholar
  7. 7.
    Chiu, P.H., Kao, G.Y.M.: Personalized blog content recommender system for mobile phone users. Int. J. Human Comput. Elsevier Publications (2010)Google Scholar
  8. 8.
    Sharma, D.K., et al.: A comparative analysis of Web page ranking algorithms for Web personalization. Int. J. Comput. Sci. Eng. 02(08), 2670–2676 (2010)Google Scholar
  9. 9.
    Billsus, D., Pazzani, M.: The Adaptive Web: Methods and Strategies of Web Personalization, pp. 550–570. Springer, Berlin (2007)Google Scholar

Copyright information

© Springer India 2014

Authors and Affiliations

  1. 1.Department of Computer Science and Engineering, Department of Computer ApplicationsNational Institute of TechnologyTiruchirappalliIndia

Personalised recommendations