Large-Scale Data-Driven Airline Market Influence Maximization
| Autor: | Thai Le, Dongwon Lee, Seoyoung Hong, Duanshun Li, Noseong Park, Jinsung Jeon, Jing Liu |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Mathematical optimization Computer Science - Machine Learning Optimization problem Artificial neural network Mean squared error Computer science business.industry Deep learning Maximization Flow network Machine Learning (cs.LG) Artificial intelligence Gradient descent business Greedy algorithm |
| Zdroj: | KDD |
| Popis: | We present a prediction-driven optimization framework to maximize the market influence in the US domestic air passenger transportation market by adjusting flight frequencies. At the lower level, our neural networks consider a wide variety of features, such as classical air carrier performance features and transportation network features, to predict the market influence. On top of the prediction models, we define a budget-constrained flight frequency optimization problem to maximize the market influence over 2,262 routes. This problem falls into the category of the non-linear optimization problem, which cannot be solved exactly by conventional methods. To this end, we present a novel adaptive gradient ascent (AGA) method. Our prediction models show two to eleven times better accuracy in terms of the median root-mean-square error (RMSE) over baselines. In addition, our AGA optimization method runs 690 times faster with a better optimization result (in one of our largest scale experiments) than a greedy algorithm. Accepted by KDD 2021 |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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