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• W2300278319 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 both synthetic and empirical datasets known to exhibit violations of Luce's axiom. Our analysis also synthesizes several recent observations connecting the Multinomial Logit model and Markov chains; the PCMC model retains the Multinomial Logit model as a special case." @default.
• W2300278319 created "2016-06-24" @default.
• W2300278319 creator A5063895090 @default.
• W2300278319 creator A5072263832 @default.
• W2300278319 date "2016-03-08" @default.
• W2300278319 modified "2023-09-27" @default.
• W2300278319 title "Pairwise Choice Markov Chains" @default.
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• W2300278319 hasPublicationYear "2016" @default.
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