Seminar “Help Me Decide” - The World of Recommender Systems

Information
Classification: 
Master Informatik, Master Wirtschaftsinformatik
Credits: 
4
Exam: 
Seminar talk, active discussion, attendance
Regular Dates: 
Wednesday, 16:45-18:15, Room IZ 251, see schedule below
Contents
Contents: 

Within this seminar, we will have a closer look at recommender systems.

Have a look at the intro slides.

Schedule:

21.10.2009 Intro Session #1  
11.11.2009 Intro Session #2  
18.11.2009 Introduction to Recommender Systems Simon
09.12.2009 Missing at Random Philipp
06.01.2010 Automatically Building Ontologies Sebastian
13.01.2010 Latent Factor Models Abir
20.01.2010 Netflix Rongfeng
27.01.2010 Explanations Martin
03.02.2010 Discussions and Grading  

 

Materials

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