IJSTR

International Journal of Scientific & Technology Research

IJSTR@Facebook IJSTR@Twitter IJSTR@Linkedin
Home About Us Scope Editorial Board Blog/Latest News Contact Us
CALL FOR PAPERS
AUTHORS
DOWNLOADS
CONTACT
QR CODE
IJSTR-QR Code

IJSTR >> Volume 2- Issue 3, March 2013 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



Proposed Approach For Web Page Access Prediction Using Popularity And Similarity Based Page Rank Algorithm

[Full Text]

 

AUTHOR(S)

Phyu Thwe

 

KEYWORDS

Index Terms: - Markov Model, Next Page Prediction, Page Rank Algorithm, Web Log Mining, Web Usage Mining

 

ABSTRACT

Abstract: - Nowadays, the Web is an important source of information retrieval, and the users accessing the Web are from different backgrounds. The usage information about users are recorded in web logs. Analyzing web log files to extract useful patterns is called Web Usage Mining. Web usage mining approaches include clustering, association rule mining, sequential pattern mining etc. The web usage mining approaches can be applied to predict next page access. In this paper, we proposed a Page Rank-like algorithm is proposed for conducting web page access prediction. We extend the use of page rank algorithm for next page prediction with several navigational attributes, which are the similarity of the page, size of the page, access-time of the page, duration of the page and transition(two pages visits sequentially) and frequency of page and transition.

 

REFERENCES

[1] Khalil, F., J. Li and H. Wang, 2006. A framework of combining markov model with association rules for predicting web page accesses. Proceedings of the 5th Australasian Conference on Data Mining and Analystics, (AusDM'06), Australian Computer Society, Inc., pp: 177-184

[2] Khalil, F., J. Li and H. Wang, 2007. Integrating markov model with clustering for predicting web page accesses. Proceedings of the 13th Australasian World Wide Web Conference (AusWeb 2007), June 30-July 4, Coffs Harbor, Australia, pp: 1-26.

[3] Khalil, F., J. Li and H. Wang, 2008. Integrating recommendation models for improved web page prediction accuracy. Proceedings of the 31th Australasian Computer Science Conference, (ACSC'08), Wollongong, NSW, pp: 91-100.

[4] M. Deshpande and G. Karypis. Selective markov models for predicting web page accesses. ACM Trans. Internet Technol., 4:163-184, May 2004.

[5] M. Eirinaki and M. Vazirgiannis. Usage-based pagerank for web personalization. In Data Mining, Fifth IEEE International Conference on, page 8 pp., nov. 2005.

[6] M. Eirinaki, M. Vazirgiannis, and D. Kapogiannis. Web path recommendations based on page ranking and markov models. In Proceedings of the 7th annual ACM international workshop on Web information and data management, WIDM '05, pages 2-9, New York, NY, USA, 2005. ACM.

[7] Y. Z. Guo, K. Ramamohanarao, and L. Park. Personalized pagerank for web page prediction based on access time-length and frequency. In Web Intelligence, IEEE/WIC/ACM International Conference on, pages 687-690, Nov. 2007.

[8] Srivastava, J., R. Cooley, M. Deshpande and P.N. Tan, 2000. Web usage mining: Discovery and applications of usage patterns from web data. SIGKDD Explorat., 1: 12-23

[9] S. Brin, L. Page, The anatomy of a large-scale hypertextual Web search engine, Computer Networks, 30(1-7): 107-117, 1998, Proc. of WWW7 Conference

[10] N. Duhan, A. Sharma, and K. Bhatia. Page ranking algorithms: A survey. In Advance Computing Conference, 2009. IACC 2009. IEEE International, pages 1530{1537, March 2009.

[11] X.Wu, V. Kumar, J. R, Quinlan, J. Ghosh, Q. Yang, H. Motoda, G. J. McLachlan, A. Ng, B. Liu, P. S. Yu, Z. Zhou, M. Steinbach, D. J. Hand and D. Steinberg, Top 10 algorithms in data mining, Knowl Inf Syst (2008) 14:1–37 DOI 10.1007/s10115-007-0114-2

[12] Bing Liu, Web Data Mining Exploring Hyperlinks, Contents, and Usage Data, Springer-Verlag Berlin Heidelberg 2007

[13] B. D. Gunel, P. Senkul, Investigating the Effect of Duration, Page Size and Frequency on Next Page Recommendation with Page Rank Algorithm, ACM, 2011

[14] Z. PABARŠKAITĖ, Enhancements of Pre-processing, Analysis and Presentation Techniques in Web Log Mining, Doctoral dissertation was prepared at the Institute of Mathematics and Informatics in 2003–2009.

[15] Suneetha K.R, Dr. R. Krishnamoorthi, Data Preprocessing and Easy Access Retrieval of Data through Data Ware House, WCECS 2009, October 20-22, 2009, San Francisco, USA

[16] A. Papoulis. Probability, Random Variables, and Stochastic Processes. McGraw Hill, 1991.


[17] M. Jalali, N. Mustapha, A. Mamat, Md. N. B Sulaiman, A Recommender System for Online Personalization in the WUM Applications, WCECS 2009, October 20-22, 2009, San Francisco, USA

[18] P. Makkar1, P. Gulati, Dr. A.K. Sharma, A Novel Approach for Predicting User Behavior for Improving Web Performance, (IJCSE) International Journal on Computer Science and Engineering Vol. 02, No. 04, 2010, 1233-1236