Zhongze Cai, Hanzhao Wang, Kalyan Talluri, Xiaocheng Li
arXiv 19 Aug 2022 · Statistics — Machine Learning · 6 citations (OpenAlex)
arXiv:2208.09325 · PDF · DOI · OpenAlex · Extracted main text
Choice modeling has been a central topic in the study of individual preference or utility across many fields including economics, marketing, operations research, and psychology. While the vast majority of the literature on choice models has been devoted to the analytical properties that lead to managerial and policy-making insights, the existing methods to learn a choice model from empirical data are often either computationally intractable or sample inefficient. In this paper, we develop deep learning-based choice models under two settings of choice modeling: (i) feature-free and (ii) feature-based. Our model captures both the intrinsic utility for each candidate choice and the effect that the assortment has on the choice probability. Synthetic and real data experiments demonstrate the performances of proposed models in terms of the recovery of the existing choice models, sample complexity, assortment effect, architecture design, and model interpretation.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Wang, Shenhao, Baichuan Mo, Jinhua Zhao (2020) Deep neural networks for choice analysis: Architecture design with alternative-specific utility functions | 1.000 | 5 | 3 | 100% |
| 2 | Sifringer, Brian, Virginie Lurkin, Alexandre Alahi (2020) Enhancing discrete choice models with representation learning | 0.928 | 5 | 4 | 80% |
| 3 | Chen, Yi-Chun, Velibor V Misić (2022) Decision forest: A nonparametric approach to modeling irrational choice | 0.874 | 5 | 2 | 100% |
| 4 | Farias, Vivek, Srikanth Jagabathula, Devavrat Shah (2009) A data-driven approach to modeling choice | 0.874 | 5 | 2 | 100% |
| 5 | Han, Yafei, Christopher Zegras, Francisco Camara Pereira, Moshe Ben-… (2020) A neural-embedded choice model: Tastenet-mnl modeling taste heterogeneity with flexibility and interpretability | 0.843 | 5 | 3 | 60% |
| 6 | Bentz, Yves, Dwight Merunka (2000) Neural networks and the multinomial logit for brand choice modelling: a hybrid approach | 0.811 | 4 | 2 | 100% |
| 7 | Chen, Ningyuan, Guillermo Gallego, Zhuodong Tang (2021) Estimating discrete choice models with random forests | 0.737 | 3 | 3 | 67% |
| 8 | Blanchet, Jose, Guillermo Gallego, Vineet Goyal (2016) A markov chain approximation to choice modeling | 0.644 | 2 | 2 | 100% |
| 9 | McFadden, Daniel, Kenneth Train (2000) Mixed mnl models for discrete response | 0.644 | 2 | 2 | 100% |
| 10 | Simsek, A Serdar, Huseyin Topaloglu (2018) An expectation-maximization algorithm to estimate the parameters of the markov chain choice model | 0.585 | 3 | 3 | 33% |
Showing the top 10 of 33 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Amortized Inference for Correlated Discrete Choice Models via Equivariant Neural Networks | 0.405 | 1 | 1 |