Imperial College London
Alternate

DrJavierBarria

Faculty of Engineering, Department of Electrical and Electronic Engineering

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Contact

 

+44 (0)20 7594 6275j.barria Website

 
 
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Location

 

1012Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Yang:2023:10.1109/ICMLA55696.2022.00189,
author = {Yang, P and Kolbeinsson, A and Shukla, N and Barria, J},
doi = {10.1109/ICMLA55696.2022.00189},
pages = {1167--1174},
publisher = {IEEE},
title = {Deep contrastive anomaly detection for airline ancillaries prediction},
url = {http://dx.doi.org/10.1109/ICMLA55696.2022.00189},
year = {2023}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - The increasing range of ancillary products offered by airlines is making existing, static frameworks obsolete. The changing expectations of customers have created a need for more dynamic and reactive offers. In order to tailor an offer to an individual journey, it is possible to leverage similar journeys and the observed outcomes in a semi-supervised approach.In this paper, a multi-stage deep learning framework, namely Deep Ancillaries Prediction (DAP), is developed to understand personalised demand for airline ancillaries and improve pricing strategies. DAP aims to solve the overlapping distribution problem and class imbalances observed in real-world airlinedatasets. The framework incorporates a contrastive learning module to learn richer feature embeddings and an autoencoder for semi-supervised learning into one framework, and outperforms current ancillary prediction systems. The modules can be trained separately and hence, are suitable for an online learning setting. This framework is designed to be transferable to differentprediction tasks in the airline industry. Significant performance enhancements are attained compared to the current state-of-the-art algorithms.
AU  - Yang,P
AU  - Kolbeinsson,A
AU  - Shukla,N
AU  - Barria,J
DO  - 10.1109/ICMLA55696.2022.00189
EP  - 1174
PB  - IEEE
PY  - 2023///
SP  - 1167
TI  - Deep contrastive anomaly detection for airline ancillaries prediction
UR  - http://dx.doi.org/10.1109/ICMLA55696.2022.00189
UR  - http://hdl.handle.net/10044/1/100547
ER  -
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