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- W4301835187 abstract "This paper addresses the general problem of modelling and learning rank data with ties. We propose a probabilistic generative model, that models the process as permutations over partitions. This results in super-exponential combinatorial state space with unknown numbers of partitions and unknown ordering among them. We approach the problem from the discrete choice theory, where subsets are chosen in a stagewise manner, reducing the state space per each stage significantly. Further, we show that with suitable parameterisation, we can still learn the models in linear time. We evaluate the proposed models on the problem of learning to rank with the data from the recently held Yahoo! challenge, and demonstrate that the models are competitive against well-known rivals." @default.
- W4301835187 created "2022-10-05" @default.
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- W4301835187 date "2010-09-09" @default.
- W4301835187 modified "2023-09-26" @default.
- W4301835187 title "Probabilistic Models over Ordered Partitions with Application in Learning to Rank" @default.
- W4301835187 doi "https://doi.org/10.48550/arxiv.1009.1690" @default.
- W4301835187 hasPublicationYear "2010" @default.
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