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Pairwise Choice Markov Chains (PCMC) have been recently introduced toovercome limitations of choice models based on traditional axioms unable toexpress empirical observations from modern behavior economics like contexteffects occurring when a choice between two options is altered by adding athird alternative. The inference approach that estimates the transition ratesbetween each possible pair of alternatives via maximum likelihood suffers whenthe examples of each alternative are scarce and is inappropriate when newalternatives can be observed at test time. In this work, we propose anamortized inference approach for PCMC by embedding its definition into a neuralnetwork that represents transition rates as a function of the alternatives' andindividual's features. We apply our construction to the complex case of airlineitinerary booking where singletons are common (due to varying prices andindividual-specific itineraries), and context effects and behaviors stronglydependent on market segments are observed. Experiments show our networksignificantly outperforming, in terms of prediction accuracy and logarithmicloss, feature engineered standard and latent class Multinomial Logit models aswell as recent machine learning approaches.

PCMC-Net: Feature-based Pairwise Choice Markov Chains