Learning Hierarchical Riffle Independent Groupings from Rankings

Jonathan Huang and Carlos Ernesto Guestrin
27th International Conference on Machine Learning, July, 2010.


Download
  • Adobe portable document format (pdf) (2MB)
Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.

Abstract
Riffled independence is a generalized notion of probabilistic independence that has been shown to be naturally applicable to ranked data. In the riffled independence model, one assigns rankings to two disjoint sets of items independently, then in a second stage, interleaves (or riffles) the two rankings together to form a full ranking, as if by shuffling a deck of cards. Because of this interleaving stage, it is much more difficult to detect riffled independence than ordinary independence. In this paper, we provide the first automated method for discovering sets of items which are riffle independent from a training set of rankings. We show that our clustering-like algorithms can be used to discover meaningful latent coalitions from real preference ranking datasets and to learn the structure of hierarchically decomposable models based on riffled independence.

Notes

Text Reference
Jonathan Huang and Carlos Ernesto Guestrin, "Learning Hierarchical Riffle Independent Groupings from Rankings," 27th International Conference on Machine Learning, July, 2010.

BibTeX Reference
@inproceedings{Huang_2010_6575,
   author = "Jonathan Huang and Carlos Ernesto Guestrin",
   title = "Learning Hierarchical Riffle Independent Groupings from Rankings",
   booktitle = "27th International Conference on Machine Learning",
   month = "July",
   year = "2010",
}