  Distributions over rankings are used to model data in various settings such
as preference analysis and political elections. The factorial size of the space
of rankings, however, typically forces one to make structural assumptions, such
as smoothness, sparsity, or probabilistic independence about these underlying
distributions. We approach the modeling problem from the computational
principle that one should make structural assumptions which allow for efficient
calculation of typical probabilistic queries. For ranking models, "typical"
queries predominantly take the form of partial ranking queries (e.g., given a
user's top-k favorite movies, what are his preferences over remaining movies?).
In this paper, we argue that riffled independence factorizations proposed in
recent literature [7, 8] are a natural structural assumption for ranking
distributions, allowing for particularly efficient processing of partial
ranking queries.
