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Reseach Article

Semantic Information and Web based Product Recommendation System – A Novel Approach

by Sneha Y.s, G. Mahadevan
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 55 - Number 9
Year of Publication: 2012
Authors: Sneha Y.s, G. Mahadevan
10.5120/8782-2753

Sneha Y.s, G. Mahadevan . Semantic Information and Web based Product Recommendation System – A Novel Approach. International Journal of Computer Applications. 55, 9 ( October 2012), 10-14. DOI=10.5120/8782-2753

@article{ 10.5120/8782-2753,
author = { Sneha Y.s, G. Mahadevan },
title = { Semantic Information and Web based Product Recommendation System – A Novel Approach },
journal = { International Journal of Computer Applications },
issue_date = { October 2012 },
volume = { 55 },
number = { 9 },
month = { October },
year = { 2012 },
issn = { 0975-8887 },
pages = { 10-14 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume55/number9/8782-2753/ },
doi = { 10.5120/8782-2753 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:56:48.471483+05:30
%A Sneha Y.s
%A G. Mahadevan
%T Semantic Information and Web based Product Recommendation System – A Novel Approach
%J International Journal of Computer Applications
%@ 0975-8887
%V 55
%N 9
%P 10-14
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The World Wide Web is today a perennial source of immense information. There is therefore, a definite demand for automated methods that can locate, identify and retrieve information to cater to the individual's requirements, demands or whims. The internet also creates newer possibilities to organize and recommend information. Web usage mining has become popular in various business areas related with Web site development. As the scale of the Internet is getting larger and larger in recent years, we are forced to spend much time to select necessary information from large amount of web pages created. Traditionally In Web usage mining, commonly visited navigational paths are extracted in terms of Web page addresses from the Web server visit logs, and the patterns are used in various applications including recommendation. But semantic information of the Web page contents is generally not included in Web usage mining. The paper has used OWL technology to add semantics to the existing navigational paths. Results shows that our approach fetched better accuracy than the existing web based approach. This paper presents a framework for integrating semantic information along with the navigational patterns. This paper evaluated the framework and it shows promising results in terms of quality recommendation of products.

References
  1. Bettina Berendt, Andreas Hotho and Gerd Stumme, Towards Semantic Web Mining, 2002 In the Proceedings of the First International Semantic Web Conference on The Semantic Web
  2. Gediminas Adomavicius and Er Tuzhilin, "Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions",2005 IEEE Transactions on Knowledge and Data Engineering
  3. Honghua Dai, Bamshad Mobasher, "Integrating Semantic Knowledge with Web Usage Mining for Personalization", Intelligent User Interfaces, IGI Global, 2009.
  4. Bernhard Ganter and Gerd Stumme, "Creation and Merging of Ontology top-levels", Conceptual Structures for knowledge creation and communication, 2003 .
  5. G. Stumme, B. Berendt and A. Hotho, Usage Mining for and on the Semantic Web. Next Generation Data Mining, 2002 in the Proc. NSF Workshop, Baltimore,
  6. W. T. Yan, M. Jacobsen, H. Garcia-Molina, Umeshwar, "From user access patterns to dynamic hypertext linking", Computer Networks and ISDN Systems, 1996 .
  7. M. Perkowitz and O. Etzioni, "Towards adaptive Web sites", The International Journal of Computer and Telecommunications Networking,1999
  8. B. Mobasher, R. Cooley, J. Srivastava, ". Automatic personalization based on web usage mining", Communications of the ACM, 2000.
  9. M. Nakagawa, B. Mobasher "A hybrid web personalization model based on site connectivity", ACM Transactions on Internet Technology, 2007.
  10. R. Liu, V. Keselj, "Combined mining of Web server logs and web contents for classifying user navigation patterns and predicting users' future requests" , Data & Knowledge Engineering, 2007
  11. R. Baraglia, F. Silvestri, "Dynamic Personalization of Web Sites Without User Intervention", Communications of the ACM 2007
  12. R. Baraglia, F. Silvestri, 2004 An online recommender system for large Web sites, In the Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence
  13. M. Jalali, N. Mustapha, A. Mamat, Md N. Sulaiman ,OPWUMP, 2008 An architecture for online predicting in WUM-based personalization system , In the proceedings CSICC conference on Advances in Computer Science and Engineering
  14. Suleyman Salin, Pinar Senkul,2009 Using Semantic Information for Web Usage Mining Based Recommendation,In the Proceedings of ISCIS Conference on International Symposium on Computer and Information Sciences,
  15. G. Kowalski, Information Retrieval Systems: Theory and implementation.
  16. W. L. Ruzzo and M. Tompa. , 1999, a linear time algorithm for finding all maximal scoring subsequences. In Proceedings of International Conference on Intelligent Systems for Molecular Biology.
  17. Resnick, Paul and Varian Hal ," Recommender system " Communications of the ACM,1997
  18. Tran T et al, 2006, Designing recommender system for e commerce: an integration approach, In the Proceedings of the ICEC, International conference on Electronic commerce.
  19. Oren Etzioni, "The World Wide Web: Quagmire or gold mine?",Communication of the ACM,1996
  20. Liyang Yu, 2006, Introduction to the Semantic Web and Semantic Web Services.
  21. Loizou, Antonis, Srinandan 2006, Recommender systems for Semantic Web, In ECAI Recommender System Workshop.
  22. Xin Sui, Suozhu Wang, Zhaowei Li, 2009, Research on the model of integration with semantic web and agent personalized recommendation , In the Proceedings of International Conference on computer Supported Cooperative Work In Design.
Index Terms

Computer Science
Information Sciences

Keywords

WUM Semantic Web Recommender System OWL