Théo Moins
ABSTRACT
Predicting locational choices (i.e., where one chooses to sit) is a challenging task because preferences are highly heterogeneous and depend not only on the location of the seats in the environment but also on the location of others. In the present research, we propose RecSeats - a framework to predict locational choices. The framework augments individual-level discrete choice models with a convolutional neural network (CNN) which can capture higher order interactions between features of available seats. The framework is flexible and can accommodate complexity in real-world locational choice data such as variability in the number of tickets purchased and the number and locations from past purchases. Applied to both locational choice experiment data and to ticketing data from a large North-American concert hall, we show that augmenting individual-level discrete choice models with a CNN consistently provides strong predictive accuracy.
- Andrea Baldin, Trine Bille, Andrea Ellero, and Daniela Favaretto. 2018. Revenue and attendance simultaneous optimization in performing arts organizations. Journal of Cultural Economics 42, 4 (nov 2018), 677–700. https://doi.org/10.1007/s10824-018-9323-7Google Scholar
Cross Ref
- William J. Baumol and William G. Bowen. 1966. Performing Arts: The Economic Dilemma; a Study of Problems Common to Theater, Opera, Music, and Dance. Twentieth Century Fund, New York, NY.Google Scholar
- Simon J. Blanchard, Tatiana Dyachenko, and Keri L. Kettle. 2020. Locational Choices: Studying Consumer Preference for Proximity to Others in Reserved-Seating Venues. Journal of Marketing Research forthcoming (2020). https://doi.org/0022243720941525Google Scholar
- Heng-Tze Cheng, Levent Koc, Jeremiah Harmsen, Tal Shaked, Tushar Chandra, Hrishi Aradhye, Glen Anderson, Greg Corrado, Wei Chai, Mustafa Ispir, Rohan Anil, Zakaria Haque, Lichan Hong, Vihan Jain, Xiaobing Liu, and Hemal Shah. 2016. Wide & Deep Learning for Recommender Systems. In Proceedings of the 1st Workshop on Deep Learning for Recommender Systems(DLRS 2016). Association for Computing Machinery, New York, NY, USA, 7–10. https://doi.org/10.1145/2988450.2988454Google ScholarDigital Library
- Long Cheng, Xuewu Chen, Jonas [De Vos], Xinjun Lai, and Frank Witlox. 2019. Applying a random forest method approach to model travel mode choice behavior. Travel Behaviour and Society 14 (2019), 1 – 10. https://doi.org/10.1016/j.tbs.2018.09.002Google ScholarCross Ref
- A. Dosovitskiy, J. T. Springenberg, and T. Brox. 2015. Learning to generate chairs with convolutional neural networks. In 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). USA, 1538–1546.Google Scholar
- Ricard Gil and Wesley R. Hartmann. 2007. The Role and Determinants of Concession Sales in Movie Theaters: Evidence from the Spanish Exhibition Industry. Review of Industrial Organization 30, 4 (01 Jun 2007), 325–347. https://doi.org/10.1007/s11151-007-9139-7Google ScholarCross Ref
- William H. Greene and David A. Hensher. 2003. A latent class model for discrete choice analysis: contrasts with mixed logit. Transportation Research Part B: Methodological 37, 8(2003), 681 – 698. https://doi.org/10.1016/S0191-2615(02)00046-2Google ScholarCross Ref
- José M. Grisolía and Kenneth G. Willis. 2012. A latent class model of theatre demand. Journal of Cultural Economics 36, 2 (2012), 113–139. https://doi.org/10.1007/s10824-012-9158-6Google ScholarCross Ref
- Julian Hagenauer and Marco Helbich. 2017. A comparative study of machine learning classifiers for modeling travel mode choice. Expert Systems with Applications 78 (2017), 273 – 282. https://doi.org/10.1016/j.eswa.2017.01.057Google ScholarDigital Library
- Edward T. Hall. 1966. The Hidden Dimension. Doubleday, New York, NY.Google Scholar
- Yafei Han, Christopher Zegras, Francisco Camara Pereira, and Moshe Ben-Akiva. 2020. A Neural-embedded Choice Model: TasteNet-MNL Modeling Taste Heterogeneity with Flexibility and Interpretability. arxiv:econ.EM/2002.00922Google Scholar
- Pratt Hetrakul and Cinzia Cirillo. 2014. A latent class choice based model system for railway optimal pricing and seat allocation. Transportation Research Part E: Logistics and Transportation Review 61 (jan 2014), 68–83. https://doi.org/10.1016/J.TRE.2013.10.005Google Scholar
- Fei Huang and Hao Huang. 2020. Event Ticket Price Prediction with Deep Neural Network on Spatial-Temporal Sparse Data. In Proceedings of the 35th Annual ACM Symposium on Applied Computing (Brno, Czech Republic) (SAC ’20). Association for Computing Machinery, New York, NY, USA, 1013–1020. https://doi.org/10.1145/3341105.3373840Google ScholarDigital Library
- Diederik P. Kingma and Jimmy Ba. 2014. Adam: A Method for Stochastic Optimization. arxiv:cs.LG/1412.6980Google Scholar
- Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. 2012. ImageNet Classification with Deep Convolutional Neural Networks. In Advances in Neural Information Processing Systems 25, F. Pereira, C. J. C. Burges, L. Bottou, and K. Q. Weinberger (Eds.). Curran Associates, Inc., USA, 1097–1105.Google Scholar
- Alix Lhéritier, Michael Bocamazo, Thierry Delahaye, and Rodrigo Acuna-Agost. 2019. Airline itinerary choice modeling using machine learning. Journal of Choice Modelling 31 (2019), 198 – 209. https://doi.org/10.1016/j.jocm.2018.02.002Google ScholarCross Ref
- Daniel McFadden. 1973. Conditional Logit Analysis of Qualitative Choice Behaviour. In Frontiers in Econometrics, P. Zarembka (Ed.). Academic Press New York, New York, NY, USA, 105–142.Google Scholar
- Daniel McFadden and Kenneth Train. 2000. Mixed MNL models for discrete response. Journal of Applied Econometrics 15, 5 (2000), 447–470. https://doi.org/10.1002/1099-1255(200009/10)15:5<447::AID-JAE570>3.0.CO;2-1Google ScholarCross Ref
- Alejandro Mottini and Rodrigo Acuna-Agost. 2017. Deep Choice Model Using Pointer Networks for Airline Itinerary Prediction. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Halifax, NS, Canada) (KDD ’17). Association for Computing Machinery, New York, NY, USA, 1575–1583. https://doi.org/10.1145/3097983.3098005Google ScholarDigital Library
- Hyeonwoo Noh, Seunghoon Hong, and Bohyung Han. 2015. Learning Deconvolution Network for Semantic Segmentation. In Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV)(ICCV ’15). IEEE Computer Society, USA, 1520–1528. https://doi.org/10.1109/ICCV.2015.178Google ScholarDigital Library
- Augustus Odena, Vincent Dumoulin, and Chris Olah. 2016. Deconvolution and Checkerboard Artifacts. Distill (2016). https://doi.org/10.23915/distill.00003Google Scholar
- Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. 2015. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. In Advances in Neural Information Processing Systems 28, C. Cortes, N. D. Lawrence, D. D. Lee, M. Sugiyama, and R. Garnett (Eds.). Curran Associates, Inc., USA, 91–99.Google Scholar
- Brian Sifringer, Virginie Lurkin, and Alexandre Alahi. 2018. Let Me Not Lie: Learning MultiNomial Logit. arxiv:stat.ML/1812.09747Google Scholar
- Xinchang Wang, Hua Wang, and Xiaoning Zhang. 2016. Stochastic seat allocation models for passenger rail transportation under customer choice. Transportation Research Part E: Logistics and Transportation Review 96 (dec 2016), 95–112. https://doi.org/10.1016/J.TRE.2016.10.003Google Scholar
- Kenneth G. Willis and Jen D. Snowball. 2009. Investigating how the attributes of live theatre productions influence consumption choices using conjoint analysis: the example of the National Arts Festival, South Africa. Journal of Cultural Economics 33, 3 (2009), 167–183. https://doi.org/10.1007/s10824-009-9097-zGoogle ScholarCross Ref
- Yunlong Zhang and Yuanchang Xie. 2008. Travel Mode Choice Modeling with Support Vector Machines. Transportation Research Record 2076, 1 (2008), 141–150. https://doi.org/10.3141/2076-16 arXiv:https://doi.org/10.3141/2076-16Google ScholarCross Ref
- Xilei Zhao, Xiang Yan, Alan Yu, and Pascal Van Hentenryck. 2020. Prediction and behavioral analysis of travel mode choice: A comparison of machine learning and logit models. Travel Behaviour and Society 20 (2020), 22 – 35. https://doi.org/10.1016/j.tbs.2020.02.003Google ScholarCross Ref
Recommendations
Brand Effects on Choice and Choice Set Formation Under Uncertainty
This paper examines the effects of brand credibility, a central concept in information economics--based approaches to brand effects and brand equity, on consumer choice and choice set formation. We investigate the mechanisms through which credibility ...
The Effect of Package Coupons on Brand Choice
We propose a stochastic choice model, and supporting empirical analyses, to understand the effect of package coupons on brand choice. Package coupons can be broadly classified into three types: peel off coupons, on-pack coupons, and in-pack coupons. Our ...
A Segment-Level Model of Category Volume and Brand Choice
It is an everyday marketplace occurrence that brands lose and gain share. However, a brand's sales gain or loss can be attributable to very different factors, and thus understanding the sources of sales gain or loss would seem to be an important aspect ...
Comments