Constrained Market Share Maximization by Signal-Guided Optimization
Authors: Bo Hui, Yuchen Fang, Tian Xia, Sarp Aykent, Wei-Shinn Ku
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In the experiment, we empirically verify the superiority of both our prediction model and optimization approach over existing works with large-scale real-world data. |
| Researcher Affiliation | Academia | 1 Auburn University 2 Beijing University of Posts and Telecommunications |
| Pseudocode | Yes | Algorithm 1: Signal-guided two-timescale Optimization |
| Open Source Code | Yes | Our code has been released at: https://github.com/coding And BS/Airline Market. |
| Open Datasets | Yes | We conduct extensive experiments on the airline origin and destination survey (DB1B) dataset1 released by the US Department of Transportation s Bureau of Transportation Statistics (BTS), which records the flight route information in the US and releases 10% of tickets sold in the US every month of the year for research purposes. 1https://www.transtats.bts.gov/Data Index.asp |
| Dataset Splits | Yes | From the prediction perspective, we aim to learn a neural network-based model by leveraging data of the top 3 airlines in the past ten years except for the last month s data. The data of the last month is used for testing. ... To prevent over-fitting for the data of last month, we use the 10-fold cross-validation method to choose the best hyper-parameter, which means we have nine 12-month folds and one 11-month fold. Following the standard cross-validation, we randomly select nine folds to train our model and utilize the remaining fold as the validation set to tune hyper-parameters and repeat this process nine times. |
| Hardware Specification | No | The paper does not specify any particular hardware details such as GPU models, CPU models, or memory used for running the experiments. |
| Software Dependencies | No | The paper mentions the use of 'Adam optimizer' but does not specify version numbers for any software dependencies or libraries. |
| Experiment Setup | Yes | We train our market share prediction model with the learning rate of 1e-3, epochs of 500, and the Adam optimizer for updating weights. The number of structure-aware attention layers in our model is 4 and the dimension of the hidden states in our model is 64. |