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Learning Hierarchical Riffle Independent Groupings from Rankings (2010)

By: Jonathan Huang and Carlos Guestrin

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.

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Jonathan Huang and Carlos Guestrin (2010). "Learning Hierarchical Riffle Independent Groupings from Rankings." International Conference on Machine Learning (ICML 2010). pdf   talk      
BibTeX citation

@inproceedings{huang+guestrin:icml10riffle,
title = {Learning Hierarchical Riffle Independent Groupings from Rankings},
author = {Jonathan Huang and Carlos Guestrin},
booktitle = {International Conference on Machine Learning (ICML 2010)},
month = {June},
year = {2010},
address = {Haifa, Israel},
wwwfilebase = {icml2010-huang-guestrin},
wwwtopic = {Permutations}
}



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