Midterm 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 Friday, October 26. 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]

• Jigar, Dhaval, Avdhesh -  Adaptive Website Design using Caching Algorithms, by Justin L Brickell, Inderjit Dhillon, Dharmendra Modha.
• Sweta, Madhavi, Neha, Vani - Optimization of Frequent Itemset Mining on Multiple-Core Processor, by Li Liu, Eric Li, Yimin Zhang, Zhizhong Tang.
• Bharat, Raghavender, Praveen, Anand - Discovery of Significant Usage Patterns from Clusters of Clickstream Data, by Lin Lu, Margaret Dunham, Yu Meng.
• Ziba, Kanokwan, Kaleem, Kimball - Predicting Clicks: Estimating the Click-Through Rate for New Ads, by Matthew Richardson, Ewa Dominowska, Robert Ragno.
• Aslan, Armando, Hua, Jerry - Google News Personalization: Scalable Online Collaborative Filtering, by Abhinandan Das, Mayur Datar, Ashutosh Garg, and Shyam Rajaram.

[Papers]

1. Optimization of Frequent Itemset Mining on Multiple-Core Processor, by Li Liu, Eric Li, Yimin Zhang, Zhizhong Tang.
2. Predicting Clicks: Estimating the Click-Through Rate for New Ads, by Matthew Richardson, Ewa Dominowska, Robert Ragno.
3. Adaptive Website Design using Caching Algorithms, by Justin L Brickell, Inderjit Dhillon, Dharmendra Modha.
4. Discovery of Significant Usage Patterns from Clusters of Clickstream Data, by Lin Lu, Margaret Dunham, Yu Meng.
5. Google News Personalization: Scalable Online Collaborative Filtering, by Abhinandan Das, Mayur Datar, Ashutosh Garg, and Shyam Rajaram.
6. A Maximum Entropy Web Recommendation System: Combining Collaborative and Content Features, by Xin Jin, Yanzan Zhou, Bamshad Mobasher.
7. Discovery of Aggregate Usage Profiles for Web Personalization, by Bamshad Mobasher, Honghua Dai, Tao Luo, Miki Nakagawa, Yuqing Sun, Jim Wiltshire.
8. Using Mixed-Effects Modeling to Compare Different Grain-Sized Skill Models, by Mingyu Feng, Neil Heffernan, Murali Mani, and Cristina Heffernan.