ar X iv : 1 41 0 . 87 50 v 1 [ cs . L G ] 3 1 O ct 2 01 4 Learning Mixtures of Ranking Models ∗
نویسندگان
چکیده
This work concerns learning probabilistic models for ranking data in a heteroge-neous population. The specific problem we study is learning the parameters of aMallows Mixture Model. Despite being widely studied, current heuristics for thisproblem do not have theoretical guarantees and can get stuck in bad local optima.We present the first polynomial time algorithm which provably learns the param-eters of a mixture of two Mallows models. A key component of our algorithm isa novel use of tensor decomposition techniques to learn the top-k prefix in boththe rankings. Before this work, even the question of identifiability in the case of amixture of two Mallows models was unresolved.
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