recommenderlab: A Framework for Developing and Testing Recommendation Algorithms
The problem of creating recommendations given a large data base from directly elicited ratings (e.g., ratings of 1 through 5 stars) is a popular research area which was lately boosted by the Netflix Prize competition. While several libraries which implement recommender algorithms have been developed over the last decade there is still the need for a framework which facilitates research on recommender systems by providing a common development and evaluation environment. This paper describes recommenderlab which provides the infrastructure to develop and test recommender algorithms for rating data and 0-1 data in a unified framework. The Package provides basic algorithms and allows the user to develop and test his/her own algorithms in the framework via a simple registration procedure.
- Michael Hahsler (team lead, SMU)
- Derek Phanekham (SMU)
- Akshaya Aradhya (SMU)
Provides the infrastructure to test and develop recommender algorithms.
Currently the focus is on recommender systems for binary data.
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- 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.
- 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.
Acknowledgement of Support
This research was partially supported by a research grant from the NSF I/UCRC: NetCentric Software and Systems Consortium.
Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the supporting organizations.