Plackett-Luce model (PL) is one of the most popular models for preference
learning. In this paper, we consider PL with features and its mixture models,
where each alternative has a vector of features, possibly different across
agents. Such models significantly generalize the standard PL, but are not as
well investigated in the literature. We extend mixtures of PLs with features to
models that generate top-l and characterize their identifiability. We further
prove that when PL with features is identifiable, its MLE is consistent with a
strictly concave objective function under mild assumptions. Our experiments on
synthetic data demonstrate the effectiveness of MLE on PL with features with
tradeoffs between statistical efficiency and computational efficiency when l
takes different values. For mixtures of PL with features, we show that an EM
algorithm outperforms MLE in MSE and runtime.Comment: 16 pages, 2 figure