CFP last date
20 December 2024
Reseach Article

Intelligent Web Information Retrieval based on User Navigational Patterns

by Anupama Prasanth, M. Hemalatha
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 109 - Number 5
Year of Publication: 2015
Authors: Anupama Prasanth, M. Hemalatha
10.5120/19186-0673

Anupama Prasanth, M. Hemalatha . Intelligent Web Information Retrieval based on User Navigational Patterns. International Journal of Computer Applications. 109, 5 ( January 2015), 26-32. DOI=10.5120/19186-0673

@article{ 10.5120/19186-0673,
author = { Anupama Prasanth, M. Hemalatha },
title = { Intelligent Web Information Retrieval based on User Navigational Patterns },
journal = { International Journal of Computer Applications },
issue_date = { January 2015 },
volume = { 109 },
number = { 5 },
month = { January },
year = { 2015 },
issn = { 0975-8887 },
pages = { 26-32 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume109/number5/19186-0673/ },
doi = { 10.5120/19186-0673 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:44:00.847126+05:30
%A Anupama Prasanth
%A M. Hemalatha
%T Intelligent Web Information Retrieval based on User Navigational Patterns
%J International Journal of Computer Applications
%@ 0975-8887
%V 109
%N 5
%P 26-32
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The foremost mission of any information retrieval algorithm is the efficient extraction of user interests. The rapid growth of web data, intense competition and user's option to choose from several alternatives increases this issue. In this context Web usage mining can provide valuable contributions in terms of ideas and methods, as it fissures useful knowledge from the pattern of user interactions with the Web. The user interest can be identified by analyzing the access pattern of user browse, the web pages they save, collect, or print. These valuable information's are available in server logs, which can be exploited to satisfy user needs by optimizing the document-retrieval task. This article is a review conducted in the field of web usage mining and its latest works for supporting the research on efficient information retrieval based on user access pattern. This survey analyzes 25 released information retrieval models to find out the major mining techniques applied in them and also to analyze the effect of diverse parameters like feedbacks, time, content, frequency etc in information retrieval. The goal of this survey is to find the best composition of features to be included in an efficient information retrieval model. Using those features a new retrieval model is then proposed.

References
  1. Chakrabarti,S, "Data mining for hypertext: A tutorial survey", ACM SIGKDD Explorations, 2000.
  2. I-Hsien Ting, "Web Mining Techniques for On-Line Social Networks Analysis: An Overview", Studies in Computational Intelligence, 2009
  3. Kavitha Sharma, Gulshan Shrivastava and Vikas Kumar, "Web Mining: Today and Tomorrow", 3rd International Conference on Electronics Computer Technology; 2011.
  4. Kumar, Gyanendra, Neelam Duhan, and A. K. Sharma, "Page ranking based on number of visits of links of Web page", 2011 2nd International Conference on Computer and Communication Technology (ICCCT-2011), 2011.
  5. Chu-Hui Lee, "Two levels of Prediction Model for User's Browsing Behavior", International Multi-Conference of Engineers and Computer Scientists, 2008.
  6. R. Kosala and H. Blockeel, "Web mining Research: A Survey"
  7. Jaideep Srivastava, Robert Cooley, Mukund Deshpande, Pang-Ning Tang, "Web Usage Mining: Discovery and Applications of Usage Patterns from Web Data", ACM SIGKDD explorations, 2000
  8. Robert Cooley, Pang Ning Tang and Jaidfeep Srivastava, "WEBSIFT, Website Information Filter System", Supported by NSF, Jun 13, 1999
  9. Shankar K. Pal, Varun Talwar, Pabithra Mitra, "Web Mining in soft computing framework: relevance, state of the art and future directions" IEEE Transactions on Neural Networks, Volume 13, Issue 5, 2002
  10. R. Cooley, B. Mobasher, and J. Srivastava, "Web Mining: Information and Pattern Discovery on the World Wide Web", Proceeding of International Conference on Tools with Artificial Intelligence, Newport Beach, CA, 558-567, 1997.
  11. Shahnaz Parvin Nina, Md. Mahamudur Rahaman, Md. Khairul Islam Bhuiyan, Khandakar Entenam Unayes Ahmed, "Pattern Discovery of Web Usage Mining"; International Conference on Computer Technology and Development, 2009.
  12. Sanjay Kumar Malik; "Information Extraction Using Web Usage Mining , web Scrapping and Semantic Annotation"; 2011 International Conference on Computing Intelligence and Communication Networks, Oct 2011.
  13. H. K. Dai, B. Mobasher, "Using ontologies to discover domain-level web usage profiles", Proceedings of the 2nd Semantic Web Mining Workshop at ECML/PKDD 2002, Helsinki, Finland, August 2002
  14. Z. Zhang, O. Nasraoui, "Mining Search Engine Query Logs for Query Recommendation", Proceedings of the 15th International Conference on World Wide Web, May 23 – 26, 2006.
  15. Zhu, Zhen, Jing-yan Wang, Mei-lan Chen, and Ren-gen Huang, "User Interest Modeling Based on Access Behavior and its Application in Personalized Information Retrieval", 2010 3rd International Conference on Information Management Innovation Management and Industrial Engineering, 2010.
  16. Indrajit Mukherjee, Samudra Banerjee, Pradeep Kumar Gupta, P. k mahanti, V. Bhatacharya , "Efficient web information retrieval based on usage mining", 2011
  17. Liu Xuanchun, Jiancun Zhou, "Research on Knowledge based Personalized Recommendation Service System Retrieval Service", Energy Procedia; 2011.
  18. Ki Soon, Selangor, Sang Ho Lee, "Classifying web Pages using Information Extraction Patterns - Preliminary Results and Findings", LaySoongil University, Seoul, Korea.
  19. A. K. Santra, S. Jayasudha, "Classification of Web Log Data to Identify Interested Users Using Naïve Classification", International Journal of Computer Science Issues, Vol. 9, Issue 1, No 2, January 2012
  20. Hussain Tasawar, Asghar Sohail and Fong Simon, "A hierarchical cluster based preprocessing methodology for Web Usage Mining", 6th International Conference on Advanced Information Management and Service (IMS), Pp. 472-477, 2010.
  21. R. Khanchana and M. Punithavalli, "Web Usage Mining for Predicting Users' Browsing Behaviors by using FPCM Clustering", IACSIT International Journal of Engineering and Technology, Vol. 3, No. 5, October 2011
  22. Ming –Syan Chen, Jong Soo Park, Philip S. Yu, "Efficient data mining for path traversal patterns", IEEE Transactions on Knowledge and Data Engineering Vol 10, No. 2, March/April 1998.
  23. T. Kamdar, "Creating adaptive web servers using incremental web log mining", Master's thesis, Computer Science Department, University of Maryland, Baltimore County, 2001
  24. Yaxiu Yu and Xin-Wei Wang, "Web Usage Mining Based on Fuzzy Clustering", International Forum on Information Technology and Applications, Pp. 268-271, 2009.
  25. Jianxi Zhang, Peiying Zhao, Lin Shang and Lunsheng Wang, "Web usage mining based on fuzzy clustering in identifying target group", ISECS International Colloquium on Computing, Communication, Control, and Management, Vol. 4, Pp. 209-212, 2009
  26. H. R. Kim, P. K. Chan, "Learning implicit user interest hierarchy for context in personalization", Proceedings of the 2003 International Conference on Intelligent User Interfaces, ACM Press, pp. 101–108, 2003.
  27. Y. Xie, V. V. Phoha, "Web user clustering from access log using belief function", Proceedings of the First International Conference on Knowledge Capture (K-CAP 2001), ACM Press, pp. 202–208, 2001
  28. M. Rami Ghorab, Dong Zhou, Alexander O'Connor, and Vincent Wade, "Personalized Information Retrieval: Survey and Classification", Springer; Volume 23, Issue 4, Sept 2013.
  29. C. R. Anderson, "A machine learning approach to web personalization", Ph. D. thesis, University of Washington, 2002
  30. Saeedeh Maleki Dizajio, Jawed Siddiqu Yasaman Soltan Zadeh, Fazilathur Rahman; "Adaptive information retrieval system via modeling user behavior"; Journal of Ambient Intelligence and Humanized Computing, Volume 5; Issue 1 Feb 2014.
  31. Xiaogang Wang, Yan Bai, Yue Li, "An Information Retrieval Method Based On Sequential Access Patterns", Asia-Pacific Conference on Wearable Computing Systems, 2010.
  32. M. Eirinaki, M. Vazirgiannis, I. Varlamis, "Sewep: using site semantics and a taxonomy to enhance the web personalization process", Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM Press, pp. 99–108, 2003
  33. K. Chang, "An Intelligent Recommender System Using Sequential Web Access Patterns", IEEE Conference on Cybernetics and Intelligent Systems; 2005.
  34. Wang Xiao- Gang, "Web Mining based on User Access Patterns for Web Personalization", ISECS International Colloquium on Computing Communication Control and Management; Aug 2009.
  35. B. Mobasher, H. Dai, T. Luo and M. Nakagawa, "Effective personalization based on association rule discovery from web usage data", Proceedings of the 3rd ACM Workshop on Web Information and Data Management (WIDM01), 2001.
  36. W. Lin, S. A. Alvarez, C. Ruiz, "Efficient adaptive–support association rule mining for recommender systems Data Mining and Knowledge Discovery", ACM, January 1; pp. 83–105, 2002.
  37. Badrul Sarwar, George Karypis, Joseph Konstan, and John Reidl, "Item-based collaborative filtering recommendation algorithms", Proceedings of the 10th International Conference on World Wide Web (WWW'01). ACM, New York, NY, USA, 285–295, 2001.
  38. Mobasher, B. , Jain, N. , Han, E. et al. "Web mining: Pattern discovery from World Wide Web transactions", Tech Rep: TR96-050, (1996)
  39. Spiliopoulou, M. , Faulstich, L. C. : "WUM: A web utilization miner", EDBT Workshop WebDB98, Springer Verlag (1996)
  40. Rohit Agarwal, K. V. Arya, Shashi Shekhar, Rakesh Kumar; "An Efficient Weighted Algorithm for Web Information Retrieval System", 2011 International Conference on Computational Intelligence and Communication Systems, 2011
Index Terms

Computer Science
Information Sciences

Keywords

Information retrieval User navigational patterns Web usage mining Web personalization Retrieval parameters.