Dynamic Navigation of Query Results Using Biased Topic Sensitive Page Rank Algorithm

Authors

  • L Lakshmi Department of Computer Science and Engineering, MLT Institute of Technology, Hyderabad, India
  • P Bhakara Reddy Department of Computer Science and Engineering, Principal of MLT Institute of Technology, Hyderabad, India

Keywords:

Page Rank, Context Sensitive Page Rank, Biasing, Rank Sink, Link Cycles

Abstract

The major disadvantage of Page Rank is that it favors the older pages, because a new page, even a very good one will not have many links unless it is a part of an existing site. Page Rank is a global measure and is query independent. The Rank sinks problem occurs when in a network pages get in infinite link cycles. To improve the search results Topic-Sensitive Page Rank also referred to as TSPR is a context-sensitive ranking algorithm for web search developed by Taher Haveliwala. The disadvantage with topic sensitive page rank algorithm is it uses basis set is small that is it uses 16 top level categories. So we propose to improve topic sensitive page rank algorithm with best set of basis topics. Here we propose to use fine grained set of topics mainly categorized into four categories and sub categories and so on. Almost all paths end at maximum sixth level. This method results efficient results compared page rank and context sensitive topic sensitive page rank algorithms.

References

shaojie qiao , tianrui li, hong li, yan zhu, jing peng, jiangtao qiu “simrank: a page rank approach based on similarity measure”, (ıntelligent systems and knowledge engineering (ISKE), 2010 international conference)

fabrizio lamberti, andrea sanna, and claudio demartini “a relation based page rank algorithm for semantic web search engines”, (IEEE Transactions on knowledge and data engineering, vol. 21, no. 1, january 2009)

junghoo cho sourashis roy robert e. adams “page quality: in search of an unbiased web ranking”, (SIGMOD 2005 june 14-16, 2005, baltimore, maryland, usa.)

arasu, j. cho, h. garcia-molina, a. paepcke, s. raghavan: "searching the web", ACM transactions on internet technology, vol 1/1, aug. 2001 chapter 5 (ranking and link analysis)

S. BRIN, l. page: "the anatomy of a large-scale hyper textual web search engine", www 1998

chapter 2 and 4.5.1

border, kumar, maghoul, raghavan, rajagopalan, stata, tomkins, wiener: "graph structure in the web", www 2000

page, brin, motwani, winograd: "the pagerank citation ranking: bringing order to the web", stanford technical report haveliwala: "topic-sensitive pagerank", www 2002

L. Lakshmi*, Dr. P. Bhaskara Reddy, Dr. C. Shoba Bindhu “Dynamic Navigation of Query Results Based on Concept Hierarchies using Topic-Sensitive Page Rank Algorithm “ Dec 2014

Utility Analysis for Topically Biased PageRank Christian Kohlschütter. Paul-Alexandru Chirita,deWolfgang Nejdl.

W. Xing and Ali Ghorbani, “Weighted PageRank Algorithm”, Proc. Of the Second Annual Conference on Communication Networks and Services Research (CNSR ’04), IEEE, 2004.

Taher H. Haveliwala. “Topic-Sensitive Page Rank: A Context-Sensitive Ranking Algorithm for Web Search”. IEEE Transactions on Knowledge and Data Engineering, Vol. 15, No4, July/August 2003, 784-796.

R. Kosala, H. Blockeel, “Web Mining Research: A Survey”, SIGKDD Explorations, Newsletter of the ACM Special Interest Group on Knowledge Discovery and Data Mining Vol. 2, No. 1 pp 1-15, 2000.

N. Duhan, A. K. Sharma and K. K. Bhatia, “Page Ranking Algorithms: A Survey”, Proceedings of the IEEE International Conference on Advance Computing, 2009.

M. G. da Gomes Jr. and Z.Gong, “Web Structure Mining: An Introduction”, Proceedings of the IEEE International Conference on Information Acquisition, 2005

Downloads

Published

2015-03-31

How to Cite

[1]
L. Lakshmi and P. B. Reddy, “Dynamic Navigation of Query Results Using Biased Topic Sensitive Page Rank Algorithm”, Int. J. Comp. Sci. Eng., vol. 3, no. 3, pp. 93–97, Mar. 2015.

Issue

Section

Research Article