Oracle International Corporation

Denysse Diaz

Alan Paul Wood

Andrew Vakhutinsky

Jorge Luis Rivero Perez

Jean-Philippe Dumont

John Thomas Coulthurst

Sanghoon Cho

Embodiments generate a demand model for a potential hotel customer of a hotel room. Embodiments, based on features of the potential hotel customer, form a plurality of clusters, each cluster including a corresponding weight and cluster probabilities. Embodiments generate an initial estimated mixture of multinomial logit ('MNL') models corresponding to each of the plurality of clusters, the mixture of MNL models including a weighted likelihood function based on the features and the weights. Embodiments determine revised cluster probabilities and update the weights. Embodiments estimate an updated estimated mixture of MNL models and maximize the weighted likelihood function based on the revised cluster probabilities and updated weights. Based on the update weights and updated estimated mixture of MNL models, embodiments generate the demand model that is adapted to predict a choice probability of room categories and rate code combinations for the potential hotel customer.

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Sachse, TX, United States of America

Denysse Diaz