CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Implicit Measures of User Interests through Country and Predicting Users’ Future Requests in WWW

by Pragya Rajput, Joy Bhattacharjee, Roopali Soni
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 71 - Number 4
Year of Publication: 2013
Authors: Pragya Rajput, Joy Bhattacharjee, Roopali Soni
10.5120/12346-8632

Pragya Rajput, Joy Bhattacharjee, Roopali Soni . Implicit Measures of User Interests through Country and Predicting Users’ Future Requests in WWW. International Journal of Computer Applications. 71, 4 ( June 2013), 19-24. DOI=10.5120/12346-8632

@article{ 10.5120/12346-8632,
author = { Pragya Rajput, Joy Bhattacharjee, Roopali Soni },
title = { Implicit Measures of User Interests through Country and Predicting Users’ Future Requests in WWW },
journal = { International Journal of Computer Applications },
issue_date = { June 2013 },
volume = { 71 },
number = { 4 },
month = { June },
year = { 2013 },
issn = { 0975-8887 },
pages = { 19-24 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume71/number4/12346-8632/ },
doi = { 10.5120/12346-8632 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:36:38.801639+05:30
%A Pragya Rajput
%A Joy Bhattacharjee
%A Roopali Soni
%T Implicit Measures of User Interests through Country and Predicting Users’ Future Requests in WWW
%J International Journal of Computer Applications
%@ 0975-8887
%V 71
%N 4
%P 19-24
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

WWW has become the center of attraction for business transactions and hence e-commerce due to its ease of use and speed. its ability from tracking the browsing behaviour of any user to even the mouse clicks of any individual has brought the vendor and end customer closer than ever before. WWW has made it possible for vendors to advertize their products i. e. they are personalizing their product messages for individual customers at a very large scale such a phenomenon is termed as mass communication such a utility is not only applicable for e-commerce but also such personalization is aiding several web browsing activities. Any action that tailors the web experience to any individual or several users is termed as web personalization. Web personalization is that the method of customizing an internet website to the wants of specific users, taking advantage of the information noninheritable from the analysis of the user's guidance behaviour(Weblog data) in correlation with alternative data collected within the web context, namely, structure, content and user profile information. The domain of web personalization has gained importance both in the area of research and commerce. In this paper we proposed a framework of web log mining to implicit measures of user interests through Country and predicting users' future requests in WWW.

References
  1. Kosala, R. and H. Blockeel, Web Mining Research: A Survey. SIGKDD Explorations, 2000. 2(1): p. 1-15.
  2. Renáta Iváncsy, István Vajk, "Frequent Pattern Mining in Web Log Data", Acta Polytechnica Hungarica, Vol. 3, No. 1, 2006, Pp 77-90.
  3. Haibin Liu, Vlado Kes?elj . "Combined mining of Web server logs and web contents for classifying user navigation patterns and predicting users' future requests". Data & Knowledge Engineering 61 (2007) 305–330.
  4. Federico Michele Facca, Pier Luca Lanzi "Mining interesting knowledge from web logs: a survey". Data & Knowledge Engineering 53 (2005) 225–251.
  5. Han, J. and M. Kamber, Data Mining: Concepts and Techniques. 2007: Morgan Kaufmann.
  6. Arun K Pujari, Data Mining Techniques, Chapter 8, Edition-2007.
  7. Liu, B. and K. C. -C. Chang, "Special Issue on Web Content Mining". ACM SIGKDD Explorations, 6(2): Pp. 1-4, 2004.
  8. Mobasher, B. , Web Usage Mining and Personalization, in Practical Handbook of Internet Computing, M. P. Singh, Editor. 2005, CRC Press. p. 15. 1-37.
  9. Eirinaki M. , Vazirgiannis M. (2003). Web mining for web personalization. ACM Transactions on Internet Technology (TOIT), 3(1), 1-27.
  10. Srivastava, J. , et al. , Web Usage Mining: Discovery and Applications of Usage Patterns from Web Data. ACM SIKDD Explorations, 1 (2): p. 12-23. Internet Computing, M. P. Singh, Editor. 2005, CRC Press. p. 15. 1-37.
  11. http://www. apnic. net/
  12. R. Agrawal,R. Shrikanth, "Fast Algorithm for mining Association Rule" Proc. of VLDB Conference, pp. 587-559, Santigo, Chile, 1995.
  13. Tanasa, D. ; Trousse, B. ; "Data preprocessing for WUM", IEEE Potentials, Vol. 23, No. 3, Pp. 22 – 25, 2004.
  14. Srivastava, J. ;"Web Usage Mining: Discovery and Applications of Usage Patterns from Web Data". ACM SIKDD Explorations, 1(2): Pp. 12-23, 2000.
  15. R. Cooley, B. Mobasher and J. Srivastava Data preparation for mining World Wide Web browsing patters. Knowledge and Information Systems, 1(1), 1999.
  16. Nikos Koutsoupias, " Exploring Web Access Logs with Correspondence Analysis", 2nd Hellenic Conf. on AI, SETN-2002, Thessaloniki, Greece, Proceedings, Companion Volume, Pp. 229-236, 11-12 April 2002. COOLEY, R. , TAN, P-N. , AND SRIVASTAVA, J. 1999b. "WebSIFT: The web site information filter system. In Proceedings of the Web Usage Analysis and User Profiling" Workshop (WEBKDD'99), Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Boston, August).
  17. COOLEY, R. , TAN, P-N. , AND SRIVASTAVA, J. 1999b. "WebSIFT: The web site information filter system. In Proceedings of the Web Usage Analysis and User Profiling" Workshop (WEBKDD'99), Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Boston, August).
Index Terms

Computer Science
Information Sciences

Keywords

Web Log Personalization Web Usage Mining Preprocessing IP-Address Country WWW

Powered by PhDFocusTM