Computer Science Publications for Machine Learning and Revenue Management

The Airlab team conducted a literature review, compiling publications from a computer science and machine learning perspective with a focus on revenue management. 

The following are 5 featured journals that showcase the Machine Learning applications on Airline Revenue Management from an academic perspective.

[1] An, B., Chen, H., Park, N., & Subrahmanian, V. S. (2016, August). MAP: frequency-based maximization of airline profits based on an ensemble forecasting approach. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 421-430). ACM.

[2] Fiig, T., Le Guen, R., & Gauchet, M. (2018). Dynamic pricing of airline offers. Journal of Revenue and Pricing Management, 17(6), 381-393.

[3] Vowles, T. M. (2001). The “Southwest Effect” in multi-airport regions. Journal of Air Transport Management, 7(4), 251-258.

[4] Siering, M., Deokar, A. V., & Janze, C. (2018). Disentangling consumer recommendations: Explaining and predicting airline recommendations based on online reviews. Decision Support Systems, 107, 52-63.

[5] Mottini, A., Lheritier, A., Acuna-Agost, R., & Zuluaga, M.A. (2018). Understanding Customer Choices to Improve Recommendations in the Air Travel Industry. RecTour@RecSys.

The full curated list of Computer Science publications for Machine Learning and Revenue Management can be found at FIU AIRlab | Mendeley.