Web Page Prediction Techniques: A Review

International Journal of Computer Trends and Technology (IJCTT)          
© - July Issue 2013 by IJCTT Journal
Volume-4 Issue-7                           
Year of Publication : 2013
Authors :Sunil Kumar, Ms. Mala Kalra


Sunil Kumar, Ms. Mala Kalra"Web Page Prediction Techniques: A Review "International Journal of Computer Trends and Technology (IJCTT),V4(7):2062-2066 July Issue 2013 .ISSN 2231-2803.www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract: - this paper proposes a survey of Web Page Prediction Techniques. Prefetching of Web page has been widely used to reduce the access latency problem of the Web users. However, if Prefetching of Web page is not accurate and Prefetched web pages are not visited by the users in their accesses, the limited bandwidth of network and services of server will not be used efficiently and may face the access delay problem. Therefore, it is critical that we have an effective prediction method during prefetching. The Markov models have been widely used to predict and analyse user‘s navigational behaviour. All the activities of web users have been saved in web log files. The stored users’ session is used to extract popular web navigation paths and predict current users’ next web page visit.


[1] “WEB (World Wide Web)”. Available at http://compnetworking.about.com/cs/worldwideweb/g/bl def_www.htm. [Online]
[2] S.K.Madria, S.S.Bhowmick, W.K.Ng, and E.P.Lim. “ Research issues in Web data mining”. In Proceedings of Data Warehousing and Knowledge Discovery, First International Conference, DaWaK 1999.
[3] Raymond Kosala, Hendrik Blockeel, “Web Mining Research”: A Survey, ACM SIGKDD Explorations Newsletter, Volume 2 Issue 1, June 2000.
[4] Wang Jicheng, Huang Yuan, Wu Gangshan, Zhang Fuyan. “Web mining: knowledge discovery on the Web”. In Proceedings of Systems, Man, and Cybernetics IEEE SMC Conference, 1999.
[5] Jaideep Srivastava, Robert Cooley, Mukund Deshpande, Pag-Ning Tan, “Web Usage Mining: Discovery and Applications of Usage Patterns from Web Data”, ACM SIGKDD Explorations Newsletter, Volume 1 Issue January 2000.
[6] S. Chakrabarti, B. E. Dom, S. R. Kumar, P. Raghavan, S. Rajagopalan, A.Tomkins, D. Gibson, and J. Kleinberg, “Mining the Web’s link structure”. Computer, 32(8):60– 67, 1999.
[7] M. Deshpande, G. Karypis, “Selective Markov Models for Predicting Web Page Accesses,” ACM transactions on Internet Technology, volume 4, No.2, pp.163-184, May 2004
[8] Teknomo, Kardi. “K-Means Clustering”. Conferences in Research and Practice in Information Technology, Volume 74. July 2007.
[9] T. Joachims, D. Freitag, and T. Mitchell, “WebWatcher: A tour guide for the World Wide Web”, in Proceedings of IJCAI, pp. 770–777, 1999.
[10] Z. Su, Q. Yang, Y. Lu, and H. Zhang, “WhatNext: A prediction system for Web requests using n-gram sequence models”, in Proceedings of 1st Int. Conference Web Inf. Syst. Eng. Conference, Hong Kong pp. 200–207, Jun. 2000.

Keywords : — Web Usage Mining, Clustering, Markov Model, User Sessions, N-Grams.