Personalized Menu Optimization with Preference Updater: A Boston Case Study
Abstract
This paper presents a personalized menu optimization model with preference updater in the context of an innovative Smart Mobility system that offers a personalized menu of travel options with incentives for each incoming traveler in real time. This Smart Mobility system can serve as a major travel demand management system that encourages energy-efficient travel options. The personalized menu optimization is built on a logit mixture model that captures each individual traveler’s choice behavior. The personalized menu optimization model is enhanced with a preference updater that can update the estimates of individual traveler’s preference parameters when new choice data is received. To illustrate the advantages of the proposed methodology, a case study is presented based on real travelers and trips in the greater Boston area from the Massachusetts Travel Survey data. The case study consists of two parts. In the first part, the personalized menu optimization with preference updater is tested in a setting where the travelers are new to the system and their preferences are updated through preference updater. A comparative analysis of the performance of the proposed method with preference updater is presented against the method without preference updater. In the second part, the benefit of using individual level preference parameters instead of population level preference parameters in the personalized menu optimization model is analyzed. The case study shows that the proposed method can outperform the hit rates of its two counterparts.
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References
1. Rayle L., Shaheen S., Chan N., Dai D., Cervero R. App-Based, On-Demand Ride Services: Comparing Taxi and Ridesourcing Trips and User Characteristics in San Francisco. Presented at 94th Annual Meeting of the Transportation Research Board, Washington, D.C., USA, 2015.
2. Atasoy B., Ikeda T., Song X., Ben-Akiva M. The Concept and Impact Analysis of a Flexible Mobility on Demand System. Transportation Research Part C: Emerging Technologies, Vol. 56, 2015, pp. 373–392.
3. Atasoy B., Ikeda T., Ben-Akiva M. Optimizing a Flexible Mobility on Demand System. Transportation Research Record (TRR), Vol. 2536, 2015, pp. 76–85.
4. Song X., Atasoy B., Ben-Akiva M. Smart Mobility Through Personalized Menu Optimization. Presented at 96th Annual Meeting of the Transportation Research Board, Washington, D.C., USA, 2017.
5. Chaptini B. Use of Discrete Choice Models with Recommender Systems. PhD dissertation. Department of Civil and Environmental Engineering, MIT, 2005.
6. Polydoropoulou A., Lambrou M. Development of an e-Learning Recommender System Using Discrete Choice Models and Bayesian Theory: A Pilot Case in the Shipping Industry. In Security Enhanced Applications for Information Systems, 2012, ISBN:.
7. Jiang H., Qi X., Sun H. Choice-Based Recommender Systems: A Unified Approach to Achieving Relevancy and Diversity. Operations Research, Vol. 62, No. 5, 2014, pp. 973–993.
8. Allenby G., Ginter J. Using Extremes to Design Products and Segment Markets. Journal of Marketing Research, Vol. 32, 1995, pp. 392–403.
9. Rossi P., McCulloch R., Allenby G. On the Value of Household Purchase History Information in Target Marketing. Marketing Science, Vol. 15, No. 4, 1996, pp. 321–340.
10. Ansari A., Essegaier S., Kohli R. Internet Recommendation Systems. Journal of Marketing Research, Vol. XXXVII, 2000, pp. 363–375.
11. Désir A., Goyal V., Segev D., Ye C. Capacity Constrained Assortment Optimization Under the Markov Chain Based Choice Model. Technical report, Columbia University, 2015. http://www.columbia.edu/~vg2277/constrained_MC.pdf.
12. Davis J., Gallego G., Topaloglu H. Assortment Planning Under the Multinomial Logit Model with Totally Unimodular Constraint Structures. Department of IEOR, Columbia University, 2013. http://www.columbia.edu/gmg2/logit_const.pdf.
13. Davis J. M., Gallego G., Topaloglu H. Assortment Optimization Under Variants of the Nested Logit Model. Operations Research, Vol. 62, No. 2, 2014, pp. 250–273.
14. Feldman J., Topaloglu H. Bounding Optimal Expected Revenues for Assortment Optimization Under Mixtures of Multinomial Logits. Production and Operations Management, Vol. 24, No. 10, 2015, pp. 1598–1620.
15. Kök A. G., Fisher M. L., Vaidyanathan R. Assortment Planning: Review of Literature and Industry Practice. In Retail Supply Chain Management, Springer US, 2008, pp. 99–153.
16. Bubeck S., Cesa-Bianchi N. Regret Analysis of Stochastic and Nonstochastic Multi-Armed Bandit Problems. Foundations and Trends in Machine Learning, Vol. 5, No. 1, 2012, pp. 1–122.
17. Gittins J., Glazebrook K., Weber R. Multi-Armed Bandit Allocation Indices. John Wiley & Sons, New York, 2011.
18. Li L., Chu W., Langford J., Schapire R. E. A Contextual-Bandit Approach to Personalized News Article Recommendation. Proc., 19th International Conference on World Wide Web, ACM, 2010, pp. 661–670.
19. Schwartz E. M., Bradlow E. T., Fader P. S. Customer Acquisition Via Display Advertising Using Multi-Armed Bandit Experiments. Marketing Science, Vol. 36, No. 4, 2017, pp. 200–522.
20. Song X. A Bayesian Bandit Approach to Personalized Online Coupon Recommendations. Master thesis, Massachusetts Institute of Technology, 2016.
21. Danaf M., Becker F., Song X., Atasoy B., Ben-Akiva M. Personalized Recommendations using Discrete Choice Models with Inter-and Intra-Consumer Heterogeneity. Working Paper, MIT, 2017.
22. Zhao F., Pereira F. C., Ball R., Kim Y., Han Y., Zegras C., Ben-Akiva M. Exploratory Analysis of a Smartphone-Based Travel Survey in Singapore. Transportation Research Record: Journal of the Transportation Research Board, 2015. 2494: 45–56.
23. Ben-Akiva M. E. Structure of Passenger Travel Demand Models. Doctoral dissertation, Massachusetts Institute of Technology, 1973.
24. Allenby G. M., Rossi P. E. Marketing Models of Consumer Heterogeneity. Journal of Econometrics, Vol. 89, No. 1, 1998, pp. 57–78.
25. Orme B., Baker G. Comparing Hierarchical Bayes Draws and Randomized First Choice for Conjoint Simulations. Sawtooth Software Research Paper Series, 2000.
26. Ben-Akiva M., McFadden D., Train K. Foundations of Stated Preference Elicitation, Consumer Choice Behavior and Choice-Based Conjoint Analysis. Working Paper, University of California, Berkeley, 2015.
27. Becker F., Danaf M., Song X., Atasoy B., Ben-Akiva M. Hierarchical Bayes Estimator of a Logit Mixture with Inter- and Intra-Consumer Heterogeneity. Working paper, MIT, 2016.
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Article first published online: April 27, 2018
Issue published: December 2018
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