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Published
Jul 17, 2014
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Abstract

Personalization is a subclass of information filtering system that seek to predict the 'ratings' or 'preferences' that a user would give to an items, they had not yet considered, using a model built from the characteristics of an item (content-based approaches or collaborative filtering approaches). Web mining is an emerging field of data mining used to provide personalization on the web. It consist three major categories i.e. Web Content Mining, Web Usage Mining, and Web Structure Mining. In this paper, a simplified Personalized Recommendation System using web usage mining and content mining is proposed to provide personalized search results to learners. The web usage mining and content mining technologies are used for system implementation aim to identify personalized recommendations. Basically, It consist four major steps to construct personalized recommendations are Data Collection, Data Processing, Data Analysis, and Output Generation. The proposed architecture in this paper is implemented using .Net technology to achieve system goals.


Keywords: Recommendations, Web Usage Mining, Association Rule Mining, Clustering, Lingo.

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Author Biographies

Namdev Ashok Anwat, Matoshri College of Engineering and Research Center, Nashik. University of Pune, Pune.

PG Student

Department of Computer Engineering

Mrs. Varsha Patil, Matoshri College of Engineering and Research Center, Nashik Near odha gaon, Eklahare, Nashik, Maharashtra - 422105, India University of Pune, Pune

Department of Computer Engineering
How to Cite
Anwat, N. A., & Patil, M. V. (2014). Personalized Recommendations in e-Learning System using Web Mining. International Journal of Emerging Trends in Science and Technology, 1(05). Retrieved from http://igmpublication.org/ijetst.in/index.php/ijetst/article/view/216

References

[1] Magdalini Eirinaki and Michalis Vazirgiannis, “Web mining for web personalization”, ACM Transactions on Internet Technology, 03(01):1-27, February 2003.
[2] Raymond Kosala and Hendrik Blockeel, “Web mining research: A survey”, SIGKDD Explorations, pages 95-104, July 2000.
[3] Nasraoui O., Frigui H., Krishnapuram R., and Joshi A, “Extracting web user profiles using relational competitive fuzzy clustering”, IJAI Knowledge Discovery, 09(04):8-14, April 2000.
[4] Mobasher B., Cooley R., and Srivastava J, “Automatic personalization based on web usage mining”, ACM Communication, 43(08):142-151, August 2000.
[5] Berendt B, “Understanding web usage at different levels of abstraction: Coarsening and visualizing sequences”, ACM SIGKDD Knowledge discovery & Data mining, 04(07):104-108, August 2001.
[6] Yuewu Dong and Jiangtao Li., “Personalized distance education system based on web mining”, IEEE Education and Information Technology, 02(05):187-191, August 2010.
[7] Michael Azmy, “Web content mining research: A survey”, ACM SIGMOD Explorations, 01(01):203-212, November 2005.
[8] Jaideep Srivastava, Robert Cooley, Mukund Deshpande, and Pangning Tan, “Web usage mining: Discovery and applications of usage patterns from web data”, ACM SIGKDD Explorations, 01(03):187-192, January 2000.
[9] Robert Cooley, Bamshad Mobasher, and Jaideep Srivastava, “Data preparation for mining world wide web browsing patterns”, Knowledge and Information Systems, 01(01):84-89, February 1999.
[10] Sasa Bosnjak, Mirjana Maric, Zita Bosnjak, “The role of web usage mining in web application evaluation”, Management Information Systems, Vol. 05(01):31-36, 2010.
[11] Borges J. and Levene M, “Data mining of user navigation patterns”, Springer-Verlag, 1836(08):92-111, April 1999.
[12] Maurice D. Mulvenna, Sarabjot S Anand, and Alex G. Buchner, “Personalization on the net using web mining”, ACM Communication, 43(08):122-128, August 2000.
[13] Margaret H. Dunham, “Data Mining: Introductory and Advanced Topics”, Pearson, 01 edition, April 2006.
[14] Jiawei Han, Micheline Kamber, and Jian Pei, “Data Mining: Concepts and Techniques”, Morgan Kaufmann, 03 edition, May 2007.