What's your choice? Learning the mixed multi-nomial logit model

SIGMETRICS 2014 - Proceedings of the 2014 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems

Volumn , Issue , 2014, Pages 565-566

  Ammar, Ammar ^a   Oh, Sewoong ^a   Shah, Devavrat ^a   Voloch, Luis Filipe ^a  

^a NONE

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPUTER SYSTEMS; INFERENCE ENGINES; INTERNET; MIXTURES; POPULATION STATISTICS;

CONSUMER INFORMATION; DISCRETE CHOICE MODELS; FUNDAMENTAL LIMITATIONS; INFERENCE ALGORITHM; MIXTURE DISTRIBUTIONS; PARTIAL PREFERENCES; RECOMMENDATION ALGORITHMS; REVEALED PREFERENCE;

MATHEMATICAL MODELS;

EID: 84904358254     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2591971.2592020     Document Type: Conference Paper

References (5)

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2. V. F. Farias, S. Jagabathula, and D. Shah. A nonparametric approach to modeling choice with limited data. Management Science, 59(2):305-322, 2013.
3. D. McFadden. Conditional logit analysis of qualitative choice behavior. 1973.
4. A. Moitra and G. Valiant. Settling the polynomial learnability of mixtures of gaussians. In Foundations of Computer Science (FOCS), 2010 51st Annual IEEE Symposium on, pages 93-102. IEEE, 2010.
5. S. Negahban, S. Oh, and D. Shah. Iterative ranking from pair-wise comparisons. In NIPS, pages 2483-2491, 2012.