Fast and Accurate Inference of Plackett-Luce Models

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


Published in:
Advances in Neural Information Processing Systems 28 (NIPS 2015)
Presented at:
Neural Information Processing Systems (NIPS), Montreal, Quebec, Canada, December 7-12, 2015
Year:
2015
Keywords:
Note:
Code available at https://github.com/lucasmaystre/lsr
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 Record created 2015-11-04, last modified 2018-03-17

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