Personalizing the Web - Lyle School of Engineering
Transcript Personalizing the Web - Lyle School of Engineering
Personalizing the Web
Project 1 - Presentation
Dr. M. Dunham
A Little Motivation
• Size of the Web
• Equal Proximity
• Shift in Internet usage (once academic focus)
• Define Web personalization
• Why a personalized Web would help
• Details of online personalization
A Better Definition
• A word from the experts.
• Adaptable vs. Adaptive
• Producers and Consumers-what does
personalization mean to each (apps)
The details – 2 phase personalized view
And the experts say…
• “Any action that tailors the Web experience to
a particular user, or a set of users.” 
The process of personalization includes
gathering and storing information, analyzing
that information, and based on that analysis,
presenting a modified view to each visitor at
the right time. 
Process, not just a presentation.
Adaptable or Adaptive?
Adaptable – Systems which allow the
modification of certain parameters by the
Adaptive – Systems which adapt themselves
automatically to current user needs or
perceived requirements, create an
appropriate environment for the user, or
modify a user’s experience. 
Producers and Consumers
• Selective Presentation
2 Phases of Personalization
Not collecting user input, where does the
data come from?
Is data all we need?
1) Gathering & storing information
2) Analyzing information
3) Present modified view
• Collaborative Filtering
• Web Content Mining & Structure Mining
• Web Usage Mining
• Human Foraging Theory
• Site Map
• Information Flow
• Recommendation Engine
• Defined personalization
• Interest for producers and consumers.
• Offline & Online phases
• Personalization is here to stay.
• Machine learning and Intelligent Systems
• Integrated Internet
Bamshad Mobasher, Honghua Dai, Tao Luo, Yuqing Sun, Jiang Zhu. "Integrating Web
Usage and Content Mining for More Effective Personalization," Proc. of the Intl. Conf. on
ECommerce and Web Technologies (ECWeb). 2000.
Honghua (Kathy) Dai, Bamshad Mobasher. "A Road map to More Effective Web
Personalization: Integrating Domain Knowledge with Web Usage Mining".
Mike Perkowitz, Oren Etzioni. "Towards Adaptive Web Sites: Conceptual Framework and
Case Study," Computer Networks (Amsterdam, Netherlands: 1999). 2001.
J. L. Herlocker, J. A. Konstan, A. Borchers, and J. Riedl. "An algorithmic framework for
performing collaborative filtering," In Proceedings of the 22nd annual international ACM
SIGIR conference on Research and development in information retrieval, pages 230-237.
R. Cooley, B. Mobasher, J. Srivastava. "Web Mining: Information and Pattern Discovery
on the World Wide Web," Proceedings of the 9th IEEE International Conference on Tools
with Artificial Intelligence (ICTAI'97). 1997.
Jaideep Srivastava, Robert Cooley, Mukund Deshpande, Pang-Ning Tan. "Web Usage
Mining: Discovery and Applications of Usage Patterns from Web Data," SIGKDD
Ed H. Chi, Peter Pirolli, Kim Chen, James Pitkow. "Using Information Scent to Model User
Information Needs and Actions on the Web," Proceedings of CHI. 2001.
Peter Pirolli, Stuart K. Card. "Information Foraging". 1999.
Fergus Toolan, Nicholas Kushmerick. “Mining Web Logs for Personalized Site Maps.”
Barrett, R., Maglio, P. P., & Kellem, D. C. "How to personalize the web, Proceedings of the
ACM Conference on Human Factors in Computing Systems," (CHI '97), Atlanta, GA.
P.P. Maglio and R. Barrett. "Intermediaries personalize information streams,"
Communications of the ACM, 43(8), pp. 96-101, 2000.
Alexandros Nanopoulos, Dimitrios Katsaros, Yannis Manolopoulos. "A Data Mining
Algorithm for Generalized Web Prefetching". 2002.
Bamshad Mobasher, Robert Cooley, Jaideep Srivastava. "Automatic Personalization
Based on Web Usage Mining," Communications of the ACM. 1999.