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Neural networks and the multinomial logit for brand choice modelling: a hybrid approach

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Cited by:

  1. Hruschka, Harald & Fettes, Werner & Probst, Markus, 2004. "An empirical comparison of the validity of a neural net based multinomial logit choice model to alternative model specifications," European Journal of Operational Research, Elsevier, vol. 159(1), pages 166-180, November.
  2. Arkoudi, Ioanna & Krueger, Rico & Azevedo, Carlos Lima & Pereira, Francisco C., 2023. "Combining discrete choice models and neural networks through embeddings: Formulation, interpretability and performance," Transportation Research Part B: Methodological, Elsevier, vol. 175(C).
  3. Wang, Shenhao & Wang, Qingyi & Bailey, Nate & Zhao, Jinhua, 2021. "Deep neural networks for choice analysis: A statistical learning theory perspective," Transportation Research Part B: Methodological, Elsevier, vol. 148(C), pages 60-81.
  4. Vroomen, Bjorn & Hans Franses, Philip & van Nierop, Erjen, 2004. "Modeling consideration sets and brand choice using artificial neural networks," European Journal of Operational Research, Elsevier, vol. 154(1), pages 206-217, April.
  5. Meisam Moghimbeygi & Anahita Nodehi, 2022. "Multinomial Principal Component Logistic Regression on Shape Data," Journal of Classification, Springer;The Classification Society, vol. 39(3), pages 578-599, November.
  6. Richard Chamboko & Jorge M. Bravo, 2016. "On the modelling of prognosis from delinquency to normal performance on retail consumer loans," Risk Management, Palgrave Macmillan, vol. 18(4), pages 264-287, December.
  7. Gelhausen, Marc Christopher, 2006. "Airport and Access Mode Choice in Germany: A Generalized Neural Logit Model Approach," MPRA Paper 4236, University Library of Munich, Germany, revised Sep 2006.
  8. Reinhold Decker, 2014. "Real-Time Analysis of Online Product Reviews by Means of Multi-Layer Feed-Forward Neural Networks," International Journal of Business and Social Research, LAR Center Press, vol. 4(11), pages 60-70, November.
  9. Ortelli, Nicola & Hillel, Tim & Pereira, Francisco C. & de Lapparent, Matthieu & Bierlaire, Michel, 2021. "Assisted specification of discrete choice models," Journal of choice modelling, Elsevier, vol. 39(C).
  10. Gelhausen, Marc Christopher, 2007. "A Generalized Neural Logit Model for Airport and Access Mode Choice in Germany," MPRA Paper 4313, University Library of Munich, Germany, revised 2007.
  11. Smeele, Nicholas V.R. & Chorus, Caspar G. & Schermer, Maartje H.N. & de Bekker-Grob, Esther W., 2023. "Towards machine learning for moral choice analysis in health economics: A literature review and research agenda," Social Science & Medicine, Elsevier, vol. 326(C).
  12. Qi, Min & Yang, Sha, 2003. "Forecasting consumer credit card adoption: what can we learn about the utility function?," International Journal of Forecasting, Elsevier, vol. 19(1), pages 71-85.
  13. Roozbeh Irani-Kermani & Edward C. Jaenicke & Ardalan Mirshani, 2023. "Accommodating heterogeneity in brand loyalty estimation: application to the U.S. beer retail market," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(4), pages 820-835, December.
  14. Shenhao Wang & Baichuan Mo & Jinhua Zhao, 2020. "Theory-based residual neural networks: A synergy of discrete choice models and deep neural networks," Papers 2010.11644, arXiv.org.
  15. Jose Ignacio Hernandez & Niek Mouter & Sander van Cranenburgh, 2024. "An economically-consistent discrete choice model with flexible utility specification based on artificial neural networks," Papers 2404.13198, arXiv.org.
  16. Wang, Shenhao & Mo, Baichuan & Zhao, Jinhua, 2021. "Theory-based residual neural networks: A synergy of discrete choice models and deep neural networks," Transportation Research Part B: Methodological, Elsevier, vol. 146(C), pages 333-358.
  17. Shenhao Wang & Baichuan Mo & Jinhua Zhao, 2019. "Deep Neural Networks for Choice Analysis: Architectural Design with Alternative-Specific Utility Functions," Papers 1909.07481, arXiv.org, revised Apr 2021.
  18. Lorena Torres Lahoz & Francisco Camara Pereira & Georges Sfeir & Ioanna Arkoudi & Mayara Moraes Monteiro & Carlos Lima Azevedo, 2023. "Attitudes and Latent Class Choice Models using Machine learning," Papers 2302.09871, arXiv.org.
  19. Reinhold Decker, 2014. "Real-Time Analysis of Online Product Reviews by Means of Multi-Layer Feed-Forward Neural Networks," International Journal of Business and Social Research, MIR Center for Socio-Economic Research, vol. 4(11), pages 60-70, November.
  20. Ioanna Arkoudi & Carlos Lima Azevedo & Francisco C. Pereira, 2021. "Combining Discrete Choice Models and Neural Networks through Embeddings: Formulation, Interpretability and Performance," Papers 2109.12042, arXiv.org, revised Sep 2021.
  21. Wan-Chen Wang & Maria Manuela Santos Silva & Luiz Moutinho, 2016. "Modelling Consumer Responses to Advertising Slogans through Artificial Neural Networks," International Journal of Business and Economics, School of Management Development, Feng Chia University, Taichung, Taiwan, vol. 15(2), pages 89-116, December.
  22. Wang, Shenhao & Wang, Qingyi & Zhao, Jinhua, 2020. "Multitask learning deep neural networks to combine revealed and stated preference data," Journal of choice modelling, Elsevier, vol. 37(C).
  23. Irani-Kermani, Roozbeh & Jaenicke, Edward C., 2018. "Generalizing Variety Seeking Measurement from Brand Space to Product Attribute Space," 2018 Annual Meeting, August 5-7, Washington, D.C. 273818, Agricultural and Applied Economics Association.
  24. Wilken, Dieter & Berster, Peter & Gelhausen, Marc Christopher, 2005. "Airport Choice in Germany - New Empirical Evidence of the German Air Traveller Survey 2003," MPRA Paper 5631, University Library of Munich, Germany.
  25. Ali, Azam & Kalatian, Arash & Choudhury, Charisma F., 2023. "Comparing and contrasting choice model and machine learning techniques in the context of vehicle ownership decisions," Transportation Research Part A: Policy and Practice, Elsevier, vol. 173(C).
  26. van Wezel, M.C. & Potharst, R., 2005. "Improved customer choice predictions using ensemble methods," Econometric Institute Research Papers EI 2005-08, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  27. Lahoz, Lorena Torres & Pereira, Francisco Camara & Sfeir, Georges & Arkoudi, Ioanna & Monteiro, Mayara Moraes & Azevedo, Carlos Lima, 2023. "Attitudes and Latent Class Choice Models using Machine Learning," Journal of choice modelling, Elsevier, vol. 49(C).
  28. Phillips, Paul & Zigan, Krystin & Santos Silva, Maria Manuela & Schegg, Roland, 2015. "The interactive effects of online reviews on the determinants of Swiss hotel performance: A neural network analysis," Tourism Management, Elsevier, vol. 50(C), pages 130-141.
  29. Harald Hruschka, 2007. "Using a heterogeneous multinomial probit model with a neural net extension to model brand choice," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(2), pages 113-127.
  30. Onno Hoffmeister & Bernhard Dalheimer, 2024. "A linear model to estimate modal split in international freight transport, based on revealed preferences about cost and time saving," Journal of Shipping and Trade, Springer, vol. 9(1), pages 1-48, December.
  31. Sifringer, Brian & Lurkin, Virginie & Alahi, Alexandre, 2020. "Enhancing discrete choice models with representation learning," Transportation Research Part B: Methodological, Elsevier, vol. 140(C), pages 236-261.
  32. Han, Yafei & Pereira, Francisco Camara & Ben-Akiva, Moshe & Zegras, Christopher, 2022. "A neural-embedded discrete choice model: Learning taste representation with strengthened interpretability," Transportation Research Part B: Methodological, Elsevier, vol. 163(C), pages 166-186.
  33. Claudia Biancotti & Leandro D'Aurizio & Raffaele Tartaglia-Polcini, 2007. "A neural network architecture for data editing in the Bank of Italy�s business surveys," Temi di discussione (Economic working papers) 612, Bank of Italy, Economic Research and International Relations Area.
  34. Zhongze Cai & Hanzhao Wang & Kalyan Talluri & Xiaocheng Li, 2022. "Deep Learning for Choice Modeling," Papers 2208.09325, arXiv.org.
  35. Youssef M. Aboutaleb & Mazen Danaf & Yifei Xie & Moshe Ben-Akiva, 2021. "Discrete Choice Analysis with Machine Learning Capabilities," Papers 2101.10261, arXiv.org.
  36. Irani-Kermani, Roozbeh & Jaenicke, Edward C., 2017. "Accommodating Heterogeneity in Brand Loyalty Estimation: Application to the U.S. Beer Retail," 2017 Annual Meeting, July 30-August 1, Chicago, Illinois 258203, Agricultural and Applied Economics Association.
  37. Yafei Han & Francisco Camara Pereira & Moshe Ben-Akiva & Christopher Zegras, 2020. "A Neural-embedded Choice Model: TasteNet-MNL Modeling Taste Heterogeneity with Flexibility and Interpretability," Papers 2002.00922, arXiv.org, revised Jul 2022.
  38. Bureau Benjamin, & Duquerroy Anne, & Giorgi Julien, & Lé Mathias, & Scott Suzanne, & Vinas Frédéric, 2021. "Corporate activity in France amid the Covid-19 crisis. A granular data analysis," Working papers 823, Banque de France.
  39. Songtao Li & Ruoran Chen & Lijian Yang & Dinglong Huang & Simin Huang, 2020. "Predictive modeling of consumer color preference: Using retail data and merchandise images," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(8), pages 1305-1323, December.
  40. Li, Xi & Shi, Mengze & Wang, Xin (Shane), 2019. "Video mining: Measuring visual information using automatic methods," International Journal of Research in Marketing, Elsevier, vol. 36(2), pages 216-231.
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