Final presentations will be group presentations, and each group should consist of three or four students. Each group must choose to present one of the papers listed in the Papers section. Each group member must present at least 10 minutes, and each presentation must be at least 30 minutes and no more than 45 minutes. Please let me know what paper you choose by Wednesday, November 28. Paper selection is first-come-first-serve.

Your presentation should cover the following aspects of the paper you selected:

• What problem did the authors try to solve? And why is the problem interesting or important?
• What theories/algorithms/techniques have been proposed before? And why did they fall short of solving the problem (thus the authors had to propose a new one)?
• Explanation of the proposed theory/algorithm/technique with examples.
• Evaluation or proof of the proposed theory/algorithm/technique.

And here are some of the things I'll look for in each presenter:

• Understanding of the whole paper, not just the part you present.
• Presentation skills, i.e. the way you stand, talk, answer questions, and interact with the audience.
• Time management.

[Schedule]

• Bharat, Praveen, Dhaval - Mining Navigation History for Recommendation, by Xiaobin Fu, Jay Budzik, Kristian J. Hammond.
• Sweta, Neha, Jigar, Madhavi - A Clustering-Based Approach for Modelling User Navigation with Increased Accuracy, by Jose Borges , Mark Levene.
• Aslan, Armando, Hua, Jerry - From User Access Patterns to Dynamic Hypertext Linking, by Tak Woon Yan, Matthew Jacobsen, Hector Garcia-Molina, Umeshwar Dayal.
• Ziba, Kaleem, Kimbell, Kanokwan - Discovery of Aggregate Usage Profiles for Web Personalization, by Bamshad Mobasher, Honghua Dai, Tao Luo, Miki Nakagawa, Yuqing Sun, Jim Wiltshire.

[Papers]

1. A Maximum Entropy Web Recommendation System: Combining Collaborative and Content Features, by Xin Jin, Yanzan Zhou, Bamshad Mobasher.
2. Discovery of Aggregate Usage Profiles for Web Personalization, by Bamshad Mobasher, Honghua Dai, Tao Luo, Miki Nakagawa, Yuqing Sun, Jim Wiltshire.
3. Mining Navigation History for Recommendation, by Xiaobin Fu, Jay Budzik, Kristian J. Hammond.
4. From User Access Patterns to Dynamic Hypertext Linking, by Tak Woon Yan, Matthew Jacobsen, Hector Garcia-Molina, Umeshwar Dayal.
5. A Clustering-Based Approach for Modelling User Navigation with Increased Accuracy, by Jose Borges , Mark Levene.
6. Visual Data Mining
• Information Visualization and Visual Data Mining, by Daniel A. Keim.
• Visual Data Mining of Web Navigational Data, by Jiyang Chen, Tong Zheng, William Thorne, Osmar R. Zaiane, Randy Goebel.
• Visualizing Web Navigation Data with Polygon Graphs, by Jiyang Chen, Tong Zheng, William Thorne, Daniel Huntley, Osmar R. Zaiane, Randy Goebel.