Final
Presentations
CS520, Spring 2008
7:30pm10:00pm, Wednesday, June 4
Please upload your final presentation slides to CSNS.
One submission
from each group is sufficient. Note that file
uploading will be disabled automatically after 11:59PM
of the due date, so please
turn in your work on time.
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, May 28.
Paper selection
is firstcomefirstserve.
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.
Total points for the midterm presentation is 20, half of which is for
the presentation as a whole, and the other half is for individual
performance.
[Schedule]
 Benjie, Robert, Tam, and ChienMing  A
Maximum Entropy Web Recommendation System: Combining Collaborative and
Content Features, by Xin Jin, Yanzan Zhou, Bamshad Mobasher.

 Sweta, Guru, Grady, and Ashok  Google News
Personalization: Scalable Online Collaborative Filtering, by
Abhinandan Das, Mayur Datar, Ashutosh Garg, and Shyam Rajaram.
[Papers]
 A
Maximum Entropy Web Recommendation System: Combining Collaborative and
Content Features, by Xin Jin, Yanzan Zhou, Bamshad Mobasher.
 Google News
Personalization: Scalable Online Collaborative Filtering, by
Abhinandan Das, Mayur Datar, Ashutosh Garg, and Shyam Rajaram.
 Incorporating Contextual Information in Recommender Systems Using a Multidimensional Approach, by Gediminas Adomavicius, Ramesh Sankaranarayanan, Shahana Sen, and Alexander Tuzhilin.
 ItemBased TopN Recommendation Algorithms, by Mukund Deshpande and George Karypis.
 Eigentaste: A Constant Time Collaborative Filtering Algorithm, by Ken Goldberg, Theresa Roeder, Dhruv Gupta, and Chris Perkins.