Learning Mixed Multinomial Logit Model from Ordinal Data

Sewoong Oh, Devavrat Shah

Neural Information Processing Systems 

Motivated by generating personalized recommendations using ordinal (or preference) data, we study the question of learning a mixture of MultiNomial Logit (MNL) model, a parameterized class of distributions over permutations, from partial ordinal or preference data (e.g.

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