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Presentation
The Good, Bad and the Indifferent: Explorations in Recommender System Health
Proceedings of the International ACM Intelligent User Interfaces Beyond Personalization Recommender Systems Research Workshop
  • Benjamin J. Keller, Eastern Michigan University
  • Sun-mi Kim, Eastern Michigan University
  • N. Srinivas Vemuri, Virginia Polytechnic Institute and State University
  • Naren Ramakrishnan, Virginia Polytechnic Institute and State University
  • Saverio Perugini, University of Dayton
Document Type
Article
Publication Date
1-1-2005
Abstract
Our work is based on the premise that analysis of the connections exploited by a recommender algorithm can provide insight into the algorithm that could be useful to predict its performance in a fielded system. We use the jumping connections model defined by Mirza et al. [6], which describes the recommendation process in terms of graphs. Here we discuss our work that has come out of trying to understand algorithm behavior in terms of these graphs. We start by describing a natural extension of the jumping connections model of Mirza et al., and then discuss observations that have come from our studies, and the directions in which we are going.
Document Version
Postprint
Comments

This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in the conference proceedings.

Permission documentation is on file.

Publisher
Association for Computing Machinery
Peer Reviewed
Yes
Citation Information
Benjamin J. Keller, Sun-mi Kim, N. Srinivas Vemuri, Naren Ramakrishnan, et al.. "The Good, Bad and the Indifferent: Explorations in Recommender System Health" Proceedings of the International ACM Intelligent User Interfaces Beyond Personalization Recommender Systems Research Workshop (2005)
Available at: http://works.bepress.com/saverio_perugini/4/