CFP last date
20 December 2024
Reseach Article

A Novel Approach towards Integration of Semantic Web Mining with Link Analysis to Improve the Effectiveness of the Personalized Web

by Chanchala Joshi, Umesh Kumar Singh
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 128 - Number 11
Year of Publication: 2015
Authors: Chanchala Joshi, Umesh Kumar Singh
10.5120/ijca2015906660

Chanchala Joshi, Umesh Kumar Singh . A Novel Approach towards Integration of Semantic Web Mining with Link Analysis to Improve the Effectiveness of the Personalized Web. International Journal of Computer Applications. 128, 11 ( October 2015), 1-5. DOI=10.5120/ijca2015906660

@article{ 10.5120/ijca2015906660,
author = { Chanchala Joshi, Umesh Kumar Singh },
title = { A Novel Approach towards Integration of Semantic Web Mining with Link Analysis to Improve the Effectiveness of the Personalized Web },
journal = { International Journal of Computer Applications },
issue_date = { October 2015 },
volume = { 128 },
number = { 11 },
month = { October },
year = { 2015 },
issn = { 0975-8887 },
pages = { 1-5 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume128/number11/22914-2015906660/ },
doi = { 10.5120/ijca2015906660 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:21:19.864841+05:30
%A Chanchala Joshi
%A Umesh Kumar Singh
%T A Novel Approach towards Integration of Semantic Web Mining with Link Analysis to Improve the Effectiveness of the Personalized Web
%J International Journal of Computer Applications
%@ 0975-8887
%V 128
%N 11
%P 1-5
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

During the past few years World Wide Web has become a main source of information acquisition. The existence of such abundance of information, in combination with the dynamic and heterogeneous nature of the web, makes web site exploration a difficult process for the user. Websites personalization is the effective way to meet the requirement of efficient web navigation. This paper proposed novel technique that uses the content semantics and the structural properties of a web site in order to improve the effectiveness of web personalization. This paper presents a personalization framework CUMPW (Content & Web Usage Mining for Personalized Web) that integrates web content and web usage data with the user’s navigational patterns and represents the correlation between contents and the usage of the website. In the second part of proposed method, this paper presents a novel approach for enhancing the quality of recommendations based on the underlying structure of a web site. This paper proposed Navigational PageRank (NPR) Algorithm that suggests link analysis in effective manner for web personalization. NPR is applied to navigational graph of user session in order to determine the importance of a web page. The proposed hybrid (CUMPW + NPR) framework provides more representative predictions results than existing techniques that rely solely on usage data.

References
  1. Eirinaki M., Lampos H., Vazirgiannis M., Varlamis I., “SEWeP: Using Site Semantics and a Taxonomy to Enhancethe Web Personalization Process”, in Proc. of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD2003), Washington DC (2003).
  2. Cooley R., Mobasher B. and Srivastava J, “Data preparation for mining World Wide Web browsing patterns”, Journal of Knowledge and Information Systems 1, (1), 1999, pp 05-32.
  3. Chen, M., Park, J. and Yu, P. “Efficient data mining for path traversal patterns,” in IEEE Transactions on Knowledge and Data Engineering Vol 10, No2, March/April 1998 pp 209-221.
  4. Jalali, M., et al. “A new clustering approach based on graph partitioning for navigation patterns mining,” in International Conference on Pattern Recognition, 2008, pp.1-4.
  5. Sujatha, V. and Punithavalli. “Improved User Navigation Pattern Prediction Technique From Web Log Data,” in International Conference on Communication Technology and System Design, 2001, pp.92-99.
  6. Tug, E., Sakiroglu, M. and Arslan, A. “Automatic discovery of the sequential accesses from web log data files via a genetic algorithm,” in Knowledge Based Systems, 2006, pp.180-186.
  7. Kim, S. and Zhang, B. “Genetic mining of HTML structures for effective web document retrieval,” in Applied Intelligence 18, 2003, pp.243-256.
  8. Mobasher, B., et al., “Integrating web usage and content mining for more effective personalization,” in First International Conference on Electronic Commerce and Web Technologies, 2000, pp.165-176.
  9. Sarukkai, R.R. “Link prediction and path analysis using Markov chains,” in 9th World Wide Web conference, 1999.
  10. Kaur C.,Aggarwal R.R., ”Reference Scan Algorithm for Path Traversal Patterns”, International Journal of Computer Applications 48(7), June 2012 pp. 20-25.
  11. Page L., Brin S., Motwani R., and Winograd T. “The Pagerank Citation Ranking: bringing order to the Web”, Technical report, Stanford Dig. Lib. Tech. Project, 1998 pp.1-17.
  12. M.S. Aktas, M.A. Nacar, F. Menczer, “Personalizing PageRank Based on Domain Profile”s, in Proc. of the 6th WEBKDD Workshop, Seattle, Washington, USA , August 22 2004 pp 83-90.
  13. Sharma A., Kumar S., Singh M., “ Semantic Web Mining for Intelligent Web Personalization”, Journal of Global Research in Computer Science, Volume 2, No. 6, June 2011, pp 77-81.
Index Terms

Computer Science
Information Sciences

Keywords

Web usage mining navigational pattern link analysis personalized web