Learning Mixtures of Plackett-Luce Models from Structured Partial Orders

Zhibing Zhao, Lirong Xia

Neural Information Processing Systems 

Mixtures of ranking models have been widely used for heterogeneous preferences. However, learning a mixture model is highly nontrivial, especially when the dataset consists of partial orders. In such cases, the parameter of the model may not be even identifiable.