Uncertainty in Artificial Intelligence
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Consensus ranking under the exponential model
Marina Meila, Kapil Phadnis, Arthur Patterson, Jeff Bilmes
We analyze the generalized Mallows model, a popular exponential model over rankings. Estimating the central (or consensus) ranking from data is NP-hard. We obtain the following new results: (1) We show that search methods can estimate both the central ranking pi0 and the model parameters theta exactly. The search is n! in the worst case, but is tractable when the true distribution is concentrated around its mode; (2) We show that the generalized Mallows model is jointly exponential in (pi0; theta), and introduce the conjugate prior for this model class; (3) The sufficient statistics are the pairwise marginal probabilities that item i is preferred to item j. Preliminary experiments confirm the theoretical predictions and compare the new algorithm and existing heuristics.
Pages: 285-294
PS Link:
PDF Link: /papers/07/p285-meila.pdf
AUTHOR = "Marina Meila and Kapil Phadnis and Arthur Patterson and Jeff Bilmes",
TITLE = "Consensus ranking under the exponential model",
BOOKTITLE = "Proceedings of the Twenty-Third Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-07)",
ADDRESS = "Corvallis, Oregon",
YEAR = "2007",
PAGES = "285--294"

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