Syllabus for Proposed Course, Fall 2006
1.5 credits; no PEP points
Offered first seven weeks of the semester
Recommender systems guide people to interesting materials based on information from other people. There is a large design space of alternative ways to organize such systems. The information that other people provide may come from explicit ratings, tags, or reviews, or implicitly from how they spend their time or money. The information can be aggregated and used to select, filter, sort, or highlight items. The recommendations may be personalized to the preferences of different users.
At the end of this course, students should understand the design space of recommender systems and be able to prepare design recommendations based on needs analysis for particular installations.
In particular, students should be able to:
Recognize opportunities for recommender systems
Know important design dimensions and tradeoffs
Implicit vs. explicit ratings
General vs. personalized recommendations
Alternative algorithms for personalized recommendations
Know pitfalls and ways to deal with them
Ratings acquisition effort; herding
Intro statistics (SI 544 or equivalent).
In addition, some familiarity with matrix algebra is a plus, although I will review this material in class. If you’re looking for something to prep over the summer, do an on-line tutorial or sections of a textbook on matrix algebra, including matrix multiplication, matrix inversion, Gaussian elimination, and Eigenvectors. You can just wait until we cover it in class sessions 4-5, but we'll be going pretty fast.
There will be two short homework exercises dealing with the specifics of particular recommender algorithms. (20% of grade)
Preparation and participation—10%
Final paper—70%. The final paper will be in the format of a mock consultant’s report for a fictitious client. It will consider a potential application setting, explore the entire design space covered in the course and consider each of the known pitfalls. It will culminate in a set of design recommendations.
Resnick, Paul and Varian, Hal. Recommender Systems, introduction to special section of Communications of the ACM, March 1997, vol. 40(3).
Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P. and Riedl, J. (1994). GroupLens: An Open Architecture for Collaborative Filtering of Netnews. In CSCW '94: Conference on Computer Supported Coorperative Work (Chapel Hill, 1994), ACM, pp. 175-186.
U. Sharadanand and P. Maes, Social Information Filtering: Algorithms for Automating "Word of Mouth", Proceedings of CHI'95 - Human Factors in Computing Systems, pages 210-217, May 1995.
G. Linden, B. Smith, and J. York.
Amazon.com recommendations: Item-to-item
collaborative filtering. IEEE Internet Computing, 7:76--80, 2002.
Rakesh Agrawal, Tomasz Imielinski, Arun Swami. 1993. Mining association rules between sets of items in large databases. Proceedings of the 1993 ACM SIGMOD international conference on Management of data. Pages: 207 – 216.
K. Goldberg, T. Roeder, D. Gupta, and C. Perkins, Eigentaste: A Constant Time Collaborative Filtering Algorithm, Technical Report M00/41, Electronic Research Laboratory, University of California, Berkeley, August 2000.
C.C. Aggarwal, J. Wolf, K. Wu and P. Yu, Horting Hatches an Egg: A Graph-Theoretic Approach to Collaborative Filtering, Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 201-212, ACM Press, August 1999.
Canny, John. 2002. Collaborative filtering with privacy via factor analysis. Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval. Pages: 238 – 245.
Breese, J.; Heckerman, D. and C. Kadie, "Empirical analysis of predictive algorithms for collaborative filtering," Uncertainty in Artificial Intelligence. Proceedings of the Fourteenth Conference (1998), Morgan Kaufman, 1998, pp. 43-52.
Excerpt from Cliff Lampe thesis describing SlashDot
Time, purchases, links
The pagerank citation ranking: Bringing order to the web. L Page, S Brin, R Motwani, T Winograd - 1998
PaulAlexandru Chirita, Wolfgang Nejdl, Cristian Zamfir, Preventing shilling attacks in online recommender systems Proceedings of WIDM’05 Bremen, Germany.
Excerpt from Sunstein, Cass. Republic.com