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  4. Fast and Accurate Inference of Plackett-Luce Models
 
conference paper

Fast and Accurate Inference of Plackett-Luce Models

Maystre, Lucas  
•
Grossglauser, Matthias  
2015
Advances in Neural Information Processing Systems 28 (NIPS 2015)
Neural Information Processing Systems (NIPS)

We show that the maximum-likelihood (ML) estimate of models derived from Luce’s choice axiom (e.g., the Plackett–Luce model) can be expressed as the stationary distribution of a Markov chain. This conveys insight into several recently proposed spectral inference algorithms. We take advantage of this perspective and formulate a new spectral algorithm that is significantly more accurate than previous ones for the Plackett–Luce model. With a simple adaptation, this algorithm can be used iteratively, producing a sequence of estimates that converges to the ML estimate. The ML version runs faster than competing approaches on a benchmark of five datasets. Our algorithms are easy to implement, making them relevant for practitioners at large

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