Large-Scale Data-Driven Airline Market Influence Maximization

Duanshun Li; Jing Liu; Jinsung Jeon; Seoyoung Hong; Thai Le; Dongwon Lee; Noseong Park

doi:10.1145/3447548.3467423

Large-Scale Data-Driven Airline Market Influence Maximization

Duanshun Li, Jing Liu, Jinsung Jeon, Seoyoung Hong, Thai Le, Dongwon Lee, Noseong Park

Graduate School

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

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.

Original language	English
Title of host publication	KDD 2021 - Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
Publisher	Association for Computing Machinery
Pages	914-924
Number of pages	11
ISBN (Electronic)	9781450383325
DOIs	https://doi.org/10.1145/3447548.3467423
Publication status	Published - 2021 Aug 14
Event	27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2021 - Virtual, Online, Singapore Duration: 2021 Aug 14 → 2021 Aug 18

Publication series

Name	Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Conference

Conference	27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2021
Country/Territory	Singapore
City	Virtual, Online
Period	21/8/14 → 21/8/18

Bibliographical note

Funding Information:
Noseong Park is the corresponding author. This work of Jinsung Jeon, Seoyoung Hong, and Noseong Park was supported by the Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2020-0-01361, Artificial Intelligence Graduate School Program (Yonsei University)). The work of Thai Le and Dongwon Lee was in part supported by NSF awards #1909702, #1940076, #1934782, and #2114824.

Publisher Copyright:
© 2021 ACM.

All Science Journal Classification (ASJC) codes

Software
Information Systems

Access to Document

10.1145/3447548.3467423

Cite this

Li, D., Liu, J., Jeon, J., Hong, S., Le, T., Lee, D., & Park, N. (2021). Large-Scale Data-Driven Airline Market Influence Maximization. In KDD 2021 - Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 914-924). (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining). Association for Computing Machinery. https://doi.org/10.1145/3447548.3467423

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abstract = "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.",

author = "Duanshun Li and Jing Liu and Jinsung Jeon and Seoyoung Hong and Thai Le and Dongwon Lee and Noseong Park",

note = "Funding Information: Noseong Park is the corresponding author. This work of Jinsung Jeon, Seoyoung Hong, and Noseong Park was supported by the Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2020-0-01361, Artificial Intelligence Graduate School Program (Yonsei University)). The work of Thai Le and Dongwon Lee was in part supported by NSF awards #1909702, #1940076, #1934782, and #2114824. Publisher Copyright: {\textcopyright} 2021 ACM.; 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2021 ; Conference date: 14-08-2021 Through 18-08-2021",

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Li, D, Liu, J, Jeon, J, Hong, S, Le, T, Lee, D & Park, N 2021, Large-Scale Data-Driven Airline Market Influence Maximization. in KDD 2021 - Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Association for Computing Machinery, pp. 914-924, 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2021, Virtual, Online, Singapore, 21/8/14. https://doi.org/10.1145/3447548.3467423

Large-Scale Data-Driven Airline Market Influence Maximization. / Li, Duanshun; Liu, Jing; Jeon, Jinsung et al.
KDD 2021 - Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, 2021. p. 914-924 (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Li, Duanshun

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AU - Le, Thai

AU - Lee, Dongwon

AU - Park, Noseong

N1 - Funding Information: Noseong Park is the corresponding author. This work of Jinsung Jeon, Seoyoung Hong, and Noseong Park was supported by the Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2020-0-01361, Artificial Intelligence Graduate School Program (Yonsei University)). The work of Thai Le and Dongwon Lee was in part supported by NSF awards #1909702, #1940076, #1934782, and #2114824. Publisher Copyright: © 2021 ACM.

PY - 2021/8/14

Y1 - 2021/8/14

N2 - 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.

AB - 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.

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M3 - Conference contribution

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T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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Li D, Liu J, Jeon J, Hong S, Le T, Lee D et al. Large-Scale Data-Driven Airline Market Influence Maximization. In KDD 2021 - Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery. 2021. p. 914-924. (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining). doi: 10.1145/3447548.3467423

Large-Scale Data-Driven Airline Market Influence Maximization

Abstract

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Bibliographical note

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