Personalizing the Web Todd Lanning Project 1 Presentation

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

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

Introduction • 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. ” [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.

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]

Producers and Consumers • Producers • Marketing • Security • Usability • Consumers • Expectations • Efficiency • Selective Presentation

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

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

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

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

References [1] [2] [3] [4] [5] [6] [7] [8] 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 22 nd annual international ACM SIGIR conference on Research and development in information retrieval, pages 230 -237. 1999. R. Cooley, B. Mobasher, J. Srivastava. "Web Mining: Information and Pattern Discovery on the World Wide Web, " Proceedings of the 9 th 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 Explorations. 2000. 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.

References [9] [10] [11] [12] [13] Fergus Toolan, Nicholas Kushmerick. “Mining Web Logs for Personalized Site Maps. ” 2002. 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. 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.