1. Personalizing the Web Todd Lanning Project 1 - Presentation CSE 8331 Dr. M. Dunham

2. A Little Motivation Size of the Web Equal Proximity Shift in Internet usage (once academic focus)

3. Introduction Define Web personalization Why a personalized Web would help Details of online personalization

4. 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

5. And the experts say… “Any action that tailors the Web experience to a particular user, or a set of users.” [1] 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. [2] Process, not just a presentation.

6. Adaptable or Adaptive? Adaptable – Systems which allow the modification of certain parameters by the user. 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. [3]

7. Producers and Consumers Producers Marketing Security Usability Consumers Expectations Efficiency Selective Presentation

8. 2 Phases of Personalization Not collecting user input, where does the data come from? Is data all we need? Entire Process 1)Gathering & storing information 2)Analyzing information 3)Present modified view

9. Offline Preparation Collaborative Filtering [4] Web Content Mining & Structure Mining [5] Web Usage Mining [6] Human Foraging Theory [7,8]

10. Online Presentation Site Map [9] Information Flow [10,11] Prefetching [12] Recommendation Engine [13]

11. Summary Defined personalization Interest for producers and consumers. Offline & Online phases Personalization is here to stay. Machine learning and Intelligent Systems Integrated Internet

12. References [1] 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. [2] Honghua (Kathy) Dai, Bamshad Mobasher. "A Road map to More Effective Web Personalization: Integrating Domain Knowledge with Web Usage Mining". [3]Mike Perkowitz, Oren Etzioni. "Towards Adaptive Web Sites: Conceptual Framework and Case Study," Computer Networks (Amsterdam, Netherlands: 1999). 2001. [4]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. 1999. [5]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. [6] Jaideep Srivastava, Robert Cooley, Mukund Deshpande, Pang-Ning Tan. "Web Usage Mining: Discovery and Applications of Usage Patterns from Web Data," SIGKDD Explorations. 2000. [7] 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. [8] Peter Pirolli, Stuart K. Card. "Information Foraging". 1999.

13. References [9]Fergus Toolan, Nicholas Kushmerick. “Mining Web Logs for Personalized Site Maps.” 2002. [10]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. 1997. [11]P.P. Maglio and R. Barrett. "Intermediaries personalize information streams," Communications of the ACM, 43(8), pp. 96-101, 2000. [12]Alexandros Nanopoulos, Dimitrios Katsaros, Yannis Manolopoulos. "A Data Mining Algorithm for Generalized Web Prefetching". 2002. [13]Bamshad Mobasher, Robert Cooley, Jaideep Srivastava. "Automatic Personalization Based on Web Usage Mining," Communications of the ACM. 1999.