Representation and Elicitation of User Preferences, Machine Learning Techniques, Personalization Techniques, Applications, Survey of Current Research, Case Studies of Commercial Sites.

Semester:

January/Intersem

Rationale:

The use of the Internet to market products has provided companies with the unprecedented ability to personalize information, products, and services to individual consumers. Personalization has been shown to be an effective tool in boosting sales and increasing customer loyalty.

Catalog Description:

Representation and Elicitation of User Preferences, Machine Learning Techniques, Personalization Techniques, Applications, Survey of Current Research, Case Studies of Commercial Sites.

Credits:

3(3-0)

Prerequisite:

AT06.20 Knowledge Management and Information Retrieval or AT02.10 Data Structures and Algorithms or Consent of Instructor.

Course Outline:

Representation and Elicitation of User Preferences

1. Structuring Objectives
2. Preference Structures and Value Functions
3. Preferential Independence

Machine Learning Techniques

1. Decision Tree Induction
2. Backpropagation Neural Networks
3. Theory Refinement

Personalization Techniques

1. Collaborative Filtering
2. Content-Based Filtering
3. Rule-Based Filtering
4. Computer-Assisted Self Explication
5. Information Visualization

Applications

1. Targeted Advertising
2. Information Filtering
3. Product Search and Selection

Survey of Current Research

Case Studies of Commercial Sites

Textbook:

Course packet consisting of relevant sections from textbooks, papers from current literature, and lecture notes.

Reference Books:

T. Mitchell:

Machine Learning, McGraw-Hill, 1997.

R. Keeney and H. Raiffa:

Decisions with Multiple Objectives, Cambridge University Press, 1993.

H. Kautz:

Recommender Systems: Papers from the 1998 Workshop, American Association for Artificial Intelligence, 1998.

W. Hanson:

Principles of Internet Marketing, Chapter 7, South Western College Publishing, 1999.

Journals and Magazines:

Communications of the ACM, Special Issue on Personalization, vol 43, no 3, Aug 2000.

Journal of the ACM

Artificial Intelligence

Machine Learning

Decision Support Systems

AI Magazine

Grading System:

The course is organized in two parts: (i) lecture and (ii) reading, presentation, and discussion of papers from the current literature. Papers are selected from recent conferences. Students are required to read all papers and to write two 1-page paper analyses each week. Each student selects one paper to present in front of the class. The intention is to give students practice with the essential skills of critical reading, analysis, written expression, and oral presentation.The final grade will be computed from the following constituent parts: mid-semester (15%), final (25%), homework and paper analyses (15%), paper presentation (10%), project (25%), and project presentation (10%).Closed-book examination is used for both the mid-semester and final exam.

Instructor:

Dr. Peter Haddawy

Contact us: Olivier Nicole

Last update: Jul 2003