Potential Topics and Materials:

SURVEYS (everybody should read this)

Gediminas Adomavicius and Alexander Tuzhilin. Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions (2005)
http://dx.doi.org/10.1109/TKDE.2005.99

Miquel Montaner, Beatriz López, and Josep Lluís de la Rosa. A Taxonomy of Recommender Agents on the Internet (2003)
http://dx.doi.org/10.1023/A:1022850703159
 

BEGINNINGS

Stereotypes
--> Elaine Rich: User Modeling via Stereotypes (1979)
http://dx.doi.org/10.1207/s15516709cog0304_3

Fab and GroupLens (CF)
--> Marko Balabanović and Yoav Shoham: Fab: Content-Based, Collaborative Recommendation (1997)
http://dx.doi.org/10.1145/245108.245124
--> Joseph A. Konstan, Bradley N. Miller, David Maltz, Jonathan L. Herlocker, Lee R. Gordon, John Riedl: GroupLens: Applying Collaborative Filtering to Usenet News (1997)
http://dx.doi.org/10.1145/245108.245126

amazon.com
--> Greg Linden, Brent Smith, and Jeremy York: Amazon.com Recommendations: Item-to-Item Collaborative Filtering (2003)
http://dx.doi.org/10.1109/MIC.2003.1167344

Critiquing
--> Robin D. Burke, Kristian J. Hammond, and Benjamin C. Young: The FindMe Approach to Assisted Browsing (1997)
http://dx.doi.org/10.1109/64.608186
 

RECENT APPROACHES

SVD (+ Jester)
--> Ken Goldberg, Theresa Roeder, Dhruv Gupta and Chris Perkins: Eigentaste: A Constant Time Collaborative Filtering Algorithm (2001)
http://dx.doi.org/10.1023/A:1011419012209

Netflix
--> Robert M. Bell and Yehuda Koren: Lessons from the Netflix Prize Challenge (2007)
http://dx.doi.org/10.1145/1345448.1345465

Latent Factor Models
--> Thomas Hofmann: Latent Semantic Models for Collaborative Filtering (2004)
http://dx.doi.org/10.1145/963770.963774

Conjoint Analysis
--> Arnaud De Bruyn, John C. Liechty, Eelko K. R. E. Huizingh, and Gary L. Lilien: Offering Online Recommendations with Minimum Customer Input Through Conjoint-Based Decision Aids (2008)
http://dx.doi.org/10.1287/mksc.1070.0306

Taxonomies
--> Cai-Nicolas Ziegler, Georg Lausen, and Joseph A. Konstan: On Exploiting Classification Taxonomies in Recommender Systems (2008)
http://dx.doi.org/10.3233/AIC-2008-0430

Automatically Building Ontologies
--> Vincent Schickel-Zuber and Boi Faltings: Using Hierarchical Clustering for Learning the Ontologies used in Recommendation Systems (2007) http://dx.doi.org/10.1145/1281192.1281257

Recommendation to groups
--> Mark O’Connor, Dan Cosley, Joseph A. Konstan, and John Riedl: PolyLens: A Recommender System for Groups of Users (2001)
http://dx.doi.org/10.1007/0-306-48019-0_11
 

ISSUES

Evaluation
--> Jonathan L. Herlocker, Joseph A. Konstan, Loren G. Terveen, John T. Riedl: Evaluating Collaborative Filtering Recommender Systems (2004) http://dx.doi.org/10.1145/963770.963772

Explanation
--> Nava Tintarev and Judith Masthoff. A Survey of Explanations in Recommender Systems (2007)
http://dx.doi.org/10.1109/ICDEW.2007.4401070

Biases in Rating Schemes
--> Robin S. Poston: Using and Fixing Biased Rating Schemes (2008)
http://dx.doi.org/10.1145/1378727.1389969

Missing at Random:
--> Benjamin M. Marlin, Richard S. Zemel, Sam Roweis, and Malcolm Slaney. Collaborative Filtering and the Missing at Random Assumption (2007)
http://en.scientificcommons.org/43436776

Collaborative vs. Individual-Based Recommendation
--> Dan Ariely, John G. Lynch, Jr., and Manuel Aparicio IV. Learning by Collaborative and Individual-Based Recommendation Agents (2004)
http://dx.doi.org/10.1207/s15327663jcp1401&2_10