Learning Mixtures of Ranking Models
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چکیده
This work concerns learning probabilistic models for ranking data in a heterogeneous population. The specific problem we study is learning the parameters of a Mallows Mixture Model. Despite being widely studied, current heuristics for this problem do not have theoretical guarantees and can get stuck in bad local optima. We present the first polynomial time algorithm which provably learns the parameters of a mixture of two Mallows models. A key component of our algorithm is a novel use of tensor decomposition techniques to learn the top-k prefix in both the rankings. Before this work, even the question of identifiability in the case of a mixture of two Mallows models was unresolved.
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Probabilistic modeling of ranking data is an extensively studied problem with applications ranging from understanding user preferences in electoral systems and social choice theory, to more modern learning tasks in online web search, crowd-sourcing and recommendation systems. This work concerns learning the Mallows model – one of the most popular probabilistic models for analyzing ranking data....
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متن کاملar X iv : 1 41 0 . 87 50 v 1 [ cs . L G ] 3 1 O ct 2 01 4 Learning Mixtures of Ranking Models ∗
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...
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