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Nonparametric Discrete Choice Experiments with Machine Learning Guided Adaptive Design (2310.12026v1)

Published 18 Oct 2023 in stat.ML, cs.LG, and stat.AP

Abstract: Designing products to meet consumers' preferences is essential for a business's success. We propose the Gradient-based Survey (GBS), a discrete choice experiment for multiattribute product design. The experiment elicits consumer preferences through a sequence of paired comparisons for partial profiles. GBS adaptively constructs paired comparison questions based on the respondents' previous choices. Unlike the traditional random utility maximization paradigm, GBS is robust to model misspecification by not requiring a parametric utility model. Cross-pollinating the machine learning and experiment design, GBS is scalable to products with hundreds of attributes and can design personalized products for heterogeneous consumers. We demonstrate the advantage of GBS in accuracy and sample efficiency compared to the existing parametric and nonparametric methods in simulations.

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References (44)
  1. Sanity checks for saliency maps. Advances in neural information processing systems, 31.
  2. Hierarchical bayes models: A practitioners guide. SSRN Electronic Journal.
  3. Policy learning with observational data.
  4. Development of hybrid genetic algorithms for product line designs. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 34(1):468–483.
  5. Genetic algorithms for product design. Management Science, 42(8):1105–1117.
  6. Optimizing product line designs: Efficient methods and comparisons. Management Science, 54(9):1544–1552.
  7. Robust product line design. Operations Research, 65(1):19–37.
  8. Product aesthetic design: A machine learning augmentation. Marketing Science.
  9. Conjoint optimization: An exact branch-and-bound algorithm for the share-of-choice problem. Management Science, 52(3):435–447.
  10. DisARM: an antithetic gradient estimator for binary latent variables.
  11. Causal interaction in factorial experiments: Application to conjoint analysis. Journal of the American Statistical Association, 114(526):529–540.
  12. Estimating marketing component effects: Double machine learning from targeted digital promotions. Marketing Science.
  13. Estimating heterogeneous causal effects of High-Dimensional treatments: Application to conjoint analysis. arXiv preprint arXiv:2201.01357.
  14. Adaptive conjoint analysis: Some caveats and suggestions. Journal of Marketing Research, 28(2):215–222.
  15. Conjoint measurement-for quantifying judgmental data. Journal of Marketing research, 8(3):355–363.
  16. Conjoint analysis in consumer research: issues and outlook. Journal of consumer research, 5(2):103–123.
  17. Muprop: Unbiased backpropagation for stochastic neural networks. arXiv preprint arXiv:1511.05176.
  18. The hidden american immigration consensus: A conjoint analysis of attitudes toward immigrants. American journal of political science, 59(3):529–548.
  19. Hauser, J. R. (2011). New developments in product-line optimization.
  20. Analyzing the capabilities of the hb logit model for choice-based conjoint analysis: a simulation study. Journal of Business Economics, 90(1):1–36.
  21. Huber, J. (2005). Conjoint analysis: how we got here and where we are (an update). In Sawtooth software conference, volume 98382, page 360. Citeseer.
  22. Categorical reparameterization with Gumbel-softmax. In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings. OpenReview.net.
  23. Optimal product design using conjoint analysis: Computational complexity and algorithms. European Journal of Operational Research, 40(2):186–195.
  24. Buy 4 reinforce samples, get a baseline for free!
  25. Efficient experimental design with marketing research applications. Journal of Marketing Research, 31(4):545–557.
  26. Gradient estimation for binary latent variables via gradient variance clipping. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 37, pages 8405–8412.
  27. Design and analysis of simulated consumer choice or allocation experiments: an approach based on aggregate data. Journal of marketing research, 20(4):350–367.
  28. Simultaneous conjoint measurement: A new type of fundamental measurement. Journal of mathematical psychology, 1(1):1–27.
  29. McFadden, D. (1974). The measurement of urban travel demand. Journal of public economics, 3(4):303–328.
  30. Adaptive self-explication of multiattribute preferences. Journal of Marketing Research, 48(1):140–156.
  31. Optimal product line designing: A comparison of different discrete choice-based approaches. In EMAC 2021 Annual Conference.
  32. Contextual combinatorial bandit and its application on diversified online recommendation. In Proceedings of the 2014 SIAM International Conference on Data Mining, pages 461–469. SIAM.
  33. Off-policy bandits with deficient support. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pages 965–975.
  34. Ellipsoidal methods for adaptive choice-based conjoint analysis. Operations Research, 67(2):315–338.
  35. Active learning for non-parametric choice models. arXiv preprint arXiv:2208.03346.
  36. Probabilistic polyhedral methods for adaptive choice-based conjoint analysis: Theory and application. Marketing Science, 26(5):596–610.
  37. Polyhedral methods for adaptive choice-based conjoint analysis. Journal of Marketing Research, 41(1):116–131.
  38. Fast polyhedral adaptive conjoint estimation. Marketing Science, 22(3):273–303.
  39. Train, K. E. (2009). Discrete choice methods with simulation. Cambridge university press.
  40. Tsafarakis, S. (2016). Redesigning product lines in a period of economic crisis: a hybrid simulated annealing algorithm with crossover. Annals of Operations Research, 247:617–633.
  41. Rebar: Low-variance, unbiased gradient estimates for discrete latent variable models. In Advances in Neural Information Processing Systems, pages 2627–2636.
  42. Learning optimal individualized treatment rules from electronic health record data. IEEE International Conference on Healthcare Informatics. IEEE International Conference on Healthcare Informatics, 2016:65–71.
  43. Probabilistic best subset selection via gradient-based optimization. arXiv preprint arXiv:2006.06448.
  44. Arm: Augment-reinforce-merge gradient for stochastic binary networks. In International Conference on Learning Representations.

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