Research on Recommender Systems

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From Recommender systems by Paul Resnick and Hal R. Varian (CACM, Volume 40 , Issue 3, 1997):

IT IS OFTEN NECESSARY TO MAKE CHOICES WITHOUT SUFFICIENT personal experience of the alternatives. In everyday life, we rely on recommendations from other people either by word of mouth, recommendation letters, movie and book reviews printed in newspapers, or general surveys such as Zagat's restaurant guides.
Recommender systems assist and augment this natural social process. In a typical recommender system people provide recommendations as inputs, which the system then aggregates and directs to appropriate recipients. In some cases the primary transformation is in the aggregation; in others the system's value lies in its ability to make good matches between the recommenders and those seeking recommendations.

References

Research Links

Projects

recommenderlab, an R extension package which provides the infrastructure to test and develop recommender algorithms. Currently the focus is on recommender systems for binary data.
Our experimental Recommender System: WU Virtual University (Look out for the green exclamation marks in the produced link lists!)

Publications

[1]	Andreas Geyer-Schulz, Michael Hahsler, Andreas Neumann, and Anke Thede. Behavior-based recommender systems as value-added services for scientific libraries. In Hamparsum Bozdogan, editor, Statistical Data Mining & Knowledge Discovery, pages 433-454. Chapman & Hall / CRC, July 2003. [ at the publisher ]
[2]	Andreas Geyer-Schulz, Michael Hahsler, Andreas Neumann, and Anke Thede. An integration strategy for distributed recommender services in legacy library systems. In M. Schader, W. Gaul, and M. Vichi, editors, Between Data Science and Applied Data Analysis, Proceedings of the 26th Annual Conference of the Gesellschaft für Klassifikation e.V., University of Mannheim, July 22-24, 2002, Studies in Classification, Data Analysis, and Knowledge Organization, pages 412-420. Springer-Verlag, July 2003. [ at the publisher ]
[3]	Andreas Geyer-Schulz, Michael Hahsler, and Anke Thede. Comparing association-rules and repeat-buying based recommender systems in a B2B environment. In M. Schader, W. Gaul, and M. Vichi, editors, Between Data Science and Applied Data Analysis, Proceedings of the 26th Annual Conference of the Gesellschaft für Klassifikation e.V., University of Mannheim, July 22-24, 2002, Studies in Classification, Data Analysis, and Knowledge Organization, pages 421-429. Springer-Verlag, July 2003. [ at the publisher ]
[4]	Andreas Geyer-Schulz and Michael Hahsler. Comparing two recommender algorithms with the help of recommendations by peers. In O.R. Zaiane, J. Srivastava, M. Spiliopoulou, and B. Masand, editors, WEBKDD 2002 - Mining Web Data for Discovering Usage Patterns and Profiles 4th International Workshop, Edmonton, Canada, July 2002, Revised Papers, Lecture Notes in Computer Science LNAI 2703, pages 137-158. Springer-Verlag, 2003. (Revised version of the WEBKDD 2002 paper “Evaluation of Recommender Algorithms for an Internet Information Broker based on Simple Association Rules and on the Repeat-Buying Theory”). [ at the publisher \| .pdf ]
[5]	Andreas Geyer-Schulz, Michael Hahsler, Andreas Neumann, and Anke Thede. Recommenderdienste für wissenschaftliche Bibliotheken und Bibliotheksverbünde. In Andreas Geyer-Schulz and Alfred Taudes, editors, Informationswirtschaft: Ein Sektor mit Zukunft, Symposium 4.-5. September 2003, Wien, Österreich, Lecture Notes in Informatics (LNI) P-33, pages 43-58. Gesellschaft für Informatik, 2003. [ at the publisher ]
[6]	Andreas Geyer-Schulz and Michael Hahsler. Evaluation of recommender algorithms for an internet information broker based on simple association rules and on the repeat-buying theory. In Brij Masand, Myra Spiliopoulou, Jaideep Srivastava, and Osmar R. Zaiane, editors, Fourth WEBKDD Workshop: Web Mining for Usage Patterns & User Profiles, pages 100-114, Edmonton, Canada, July 2002. [ .pdf ]
[7]	Andreas Geyer-Schulz, Michael Hahsler, and Maximillian Jahn. A customer purchase incidence model applied to recommender systems. In R. Kohavi, B.M. Masand, M. Spiliopoulou, and J. Srivastava, editors, WEBKDD 2001 - Mining Log Data Across All Customer Touch Points, Third International Workshop, San Francisco, CA, USA, August 26, 2001, Revised Papers, Lecture Notes in Computer Science LNAI 2356, pages 25-47. Springer-Verlag, July 2002. (Revised version of the WEBKDD 2001 paper “A Customer Purchase Incidence Model Applied to Recommender Systems”). [ at the publisher \| .pdf ]
[8]	Walter Böhm, Andreas Geyer-Schulz, Michael Hahsler, and Maximillian Jahn. Repeat buying theory and its application for recommender services. In O. Opitz and M. Schwaiger, editors, Exploratory Data Analysis in Empirical Research, Proceedings of the 25th Annual Conference of the Gesellschaft für Klassifikation e.V., University of Munich, March 14-16, 2001, pages 229-239. Springer-Verlag, 2002. [ at the publisher \| .pdf ]
[9]	Wolfgang Gaul, Andreas Geyer-Schulz, Michael Hahsler, and Lars Schmidt-Thieme. eMarketing mittels Recommendersystemen. Marketing ZFP, 24:47-55, 2002. [ at the publisher ]
[10]	Andreas Geyer-Schulz, Michael Hahsler, and Maximillian Jahn. Wissenschaftliche Recommendersysteme in Virtuellen Universitäten. In H.-J. Appelrath, R. Beyer, U. Marquardt, H.C. Mayr, and C. Steinberger, editors, Unternehmen Hochschule, Wien, Österreich, September 2001. Symposium UH2001, GI Lecture Notes in Informatics (LNI). [ at the publisher \| .pdf ]
[11]	Andreas Geyer-Schulz, Michael Hahsler, and Maximillian Jahn. A customer purchase incidence model applied to recommender systems. In WEBKDD2001 Workshop: Mining Log Data Across All Customer TouchPoints, pages 35-45, San Francisco, CA, August 2001. [ .pdf ]
[12]	Andreas Geyer-Schulz, Michael Hahsler, and Maximillian Jahn. Educational and scientific recommender systems: Designing the information channels of the virtual university. International Journal of Engineering Education, 17(2):153-163, 2001. [ at the publisher \| .pdf ]
[13]	Andreas Geyer-Schulz, Michael Hahsler, and Maximillian Jahn. myvu: A next generation recommender system based on observed consumer behavior and interactive evolutionary algorithms. In Wolfgang Gaul, Otto Opitz, and Martin Schader, editors, Data Analysis: Scientific Modeling and Practical Applications, Studies in Classification, Data Analysis, and Knowledge Organization, pages 447-457. Springer Verlag, Heidelberg, Germany, 2000. [ at the publisher \| .pdf ]