publication . Preprint . 2016

Pairwise Choice Markov Chains

Ragain, Stephen; Ugander, Johan;
Open Access English
  • Published: 08 Mar 2016
Abstract
As datasets capturing human choices grow in richness and scale---particularly in online domains---there is an increasing need for choice models that escape traditional choice-theoretic axioms such as regularity, stochastic transitivity, and Luce's choice axiom. In this work we introduce the Pairwise Choice Markov Chain (PCMC) model of discrete choice, an inferentially tractable model that does not assume any of the above axioms while still satisfying the foundational axiom of uniform expansion, a considerably weaker assumption than Luce's choice axiom. We show that the PCMC model significantly outperforms the Multinomial Logit (MNL) model in prediction tasks on ...
Subjects
free text keywords: Statistics - Machine Learning, Computer Science - Artificial Intelligence
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