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"NIPS Neural Information Processing Systems 8-11th December 2014, Montreal, Canada",,, "Paper ID:","406" "Title:","Learning Mixed Multinomial Logit Model from Ordinal Data" Current Reviews First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. Summary: This paper extends the classic MultiNomial Logit (MNL) choice model to a general family of choice models named Mixed MNL, which can be seen as a parametric class of distributions over permutations (e.g., permutations of items according to user preference). The main contributions of the paper are (1) to identify sufficient conditions under which a mixed MNL can be learnt, and (2) to propose a two-phase algorithm to learn the proposed mixed MNL models in an efficient manner. Part of the interesting theoretical results shows that the model with r components can be learnt with sample size being polynomially in n (number of items of interest) and r (number of components). Quality: The problem choice modeling studied in this paper is a fundamental and critical problem to the social choice community, and the proposed model and algorithm for this problem are certainly of interest to the machine learning community.