A Robust Page Ranking Method based
on Link-Visits of Web Page

Author - 

Sonu Kumar
Niraj Singhal
Ravikant

ABSTRACT

Search engines generally return a large number of pages in response to user queries. To assist the users to navigate in the result list, ranking methods are applied on the search results. Web search engines encounter many new challenges with the increased amount of information on the web. Web documents have been a main resource for various purposes, and people rely on search engines to retrieve the desired documents. This paper proposes a dynamic and efficient Page rank algorithm for search engines to return quality results by scoring the relevance of web documents. The modified Page rank algorithm increases the degree of relevance than the original one, and decreases the time and efforts to find the desired documents from the set of results returned by search engine. Here, a page ranking mechanism called PRLV (Page Ranking based on Link Visits)is being devised for search engines, which works on the basic ranking algorithm of Google i.e. Page Rank and takes number of visits of inbound links of Web pages into account. To make rank value of pages dynamic rather than static, a new concept called PRLV is proposed and described, which takes into account users’ behaviour i.e. Link Visit Information, and calculates importance of pages.

Keywords

Search Engine, Page Rank Algorithm, PRLV

References

  • Glen Jeh and Jennifer Widom.Simrank: A measure of structural-context similarity. Technical report, Stanford University Database Group, 2001. 

  • David Cohn and Huan Chang.Learning to probabilistically identify authoritative documents. In Proc. 17th International Conf. on Machine Learning, pages 167–174. Morgan Kaufmann, San Francisco, CA, 2000. 

  • J. Carriere and R. Kazman.Webquery: Searching and visualizing the web through connectivity. In Proceedings of the International WWW Conference, 1997. 

  • JaroslavPokorny, JozefSmizansky, Page Content Rank: An Approach to the Web Content Mining. 

  • L. Page, S. Brin, R. Motwani, and T. Winograd. The pagerank citation ranking: Bringing order to the web. Technical report, Stanford Digital Libraries SIDL-WP1999- 0120, 1999. 

  • C. Ridings and M. Shishigin, Pagerank uncovered. Technical report, 2002. 

  • Jon M. Kleinberg. Authoritative sources in a hyperlinked environment. Journal of the ACM, 46(5):604–632, 1999. [8] Kleinberg, J., Authorative Sources in a Hyperlinked Environment. Proceedings of the 23rd annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 1998. 

  • C. Ding, X. He, P. Husbands, H. Zha, and H. Simon. Link analysis: Hubs and authorities on the world. Technical report:47847, 2001. 

  • Wenpu Xing and Ali Ghorbani, Weighted PageRank Algorithm, Proceedings of the Second Annual Conference on Communication Networks and Services Research (CNSR’04), 2004 IEEE. 

  • S. Chakrabarti, B. Dom, D. Gibson, J. Kleinberg, P. Raghavan, and S. Rajagopalan.Automatic resource list compilation by analyzing hyperlink structure and associated text. In Proceedings of the 7th International World Wide Web Conference, 1998.

  • Salton G. and Buckley, C., 1998. Term Weighting Approaches in Automatic Text Retrieval.In Information Processing and Management. Vol. 24, No. 5, pp. 243–223. 

  • G. Jeh and J. Widom.Simrank: A measure of structuralcontext similarity, 2002. 

  • S. Chakrabarti, B. E. Dom, S. R. Kumar, P. Raghavan, S. Rajagopalan, A. Tomkins, D. Gibson, and J. Kleinberg, Mining the Web’s link structure. Computer, 32(8):60–67, 1999. 

  • B. H. Murray and A. Moore.Sizing the internet, July 2000.

  • Andrew Y. Ng, Alice X. Zheng, and Michael I. Jordan. Stable algorithms for link analysis.In Proc. 24th Annual Intl. ACM SIGIR Conference.ACM, 2001. 

  • R. Lempel and S. Moran.The stochastic approach for linkstructure analysis (SALSA) and the TKC effect. Computer Networks (Amsterdam, Netherlands: 1999), 33(1–6):387–401,2000. 

  • Mark Levene and Richard Wheeldon.Web dynamics, 2001. [19] Peter Lyman and Hal R. Varian.How much information, 2000.

  • Jean-Loup Guillaume and MatthieuLatapy. The web graph: an overview.‘ 

  • What’s the value of data resource management? http:// www.dama.org/data facts you can use.htm. 

  • Christos H. Papadimitriou, Hisao Tamaki, PrabhakarRaghavan, and SantoshVempala. Latent semantic indexing: A probabilistic analysis. pages 159– 168, 1998 

  • C. J. Van Rijsbergen. Information Retrieval, 2nd edition.Dept. of Computer Science, University of Glasgow, 1979. 

  • Scott C. Deerwester, Susan T. Dumais, Thomas K. Landauer, George W. Furnas, and Richard A. Harshman.Indexing by latent semantic analysis.Journal of the American Society of Information Science, 41(6):391– 407, 1990.

  •  SoumenChakrabarti, Byron E. Dom, S. Ravi Kumar, PrabhakarRaghavan, Sridhar Rajagopalan, Andrew Tomkins, DavidGibson, and Jon Kleinberg. Mining the Web’s link structure.Computer, 32(8):60–67, 1999. 

  • NareshBarsagade, Web Usage Mining andPattern Discovery: A Survey Paper, CSE8331, Dec.8,2003. 

  • Eiron, N. McCurley, K., and Tomlin, J. Ranking the web frontier. Proceedings of the international conference on World Wide Web, (WWW’04). Pp.309-318, 2004