Airlines Content Recommendations Based on Passengers' Choice Using Bayesian Belief Networks

Chen, Sien; Huang, Wenqiang; Chen, Mengxi; Zhong, Junjiang; Jiecheng,

doi:10.5772/intechopen.70131

Cited by 5 publications

(2 citation statements)

References 15 publications

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“…Findings show that combining multiple methods is not always more effective than a single method, and in given temporal contexts, different approaches perform differently [ 80 ]. In the competitive airline industry, customer prediction and recommendation are active avenues of pursuit aimed at discerning customer behaviors [ 27 ], generating revenue [ 64 ], endearing customer loyalty, and enhancing customer experience [ 12 ]. In sum, predicting the behavior of airline travelers is challenging due to the confounding factors, including individual travel attributes, destinations, and the situation of the market [ 48 ].…”

Section: Prior Workmentioning

confidence: 99%

Forecasting the nearly unforecastable: why aren’t airline bookings adhering to the prediction algorithm?

Thirumuruganathan

Jung

Robillos

et al. 2021

Electron Commer Res

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Using 27 million flight bookings for 2 years from a major international airline company, we built a Next Likely Destination model to ascertain customers’ next flight booking. The resulting model achieves an 89% predictive accuracy using historical data. A unique aspect of the model is the incorporation of self-competence, where the model defers when it cannot reasonably make a recommendation. We then compare the performance of the Next Likely Destination model in a real-life consumer study with 35,000 actual airline customers. In the user study, the model obtains a 51% predictive accuracy. What happened? The Individual Behavior Framework theory provides insights into possibly explaining this inconsistency in evaluation outcomes. Research results indicate that algorithmic approaches in competitive industries must account for shifting customer preferences, changes to the travel environment, and confounding business effects rather than relying solely on historical data.

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Section: Prior Workmentioning

confidence: 99%

Forecasting the nearly unforecastable: why aren’t airline bookings adhering to the prediction algorithm?

Thirumuruganathan

Jung

Robillos

et al. 2021

Electron Commer Res

View full text Add to dashboard Cite

show abstract

“…Consider a real-time airline recommendation system to recommend the best airline from source to destination [62]. The recommendation is done via Twitter, Facebook, airline web API 1, and airline web API 2 real-time sources.…”

Section: Case Studymentioning

confidence: 99%

Towards a Novel Framework for Automatic Big Data Detection

Ahmed

Ismail

2020

IEEE Access

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Big data is a "relative" concept. It is the combination of data, application, and platform properties. Recently, big data specific technologies have emerged, including software frameworks, databases, hardware accelerators, storage technologies, etc. However, the automatic selection of these solutions for big data computations remains a non-trivial task. Presently, the big data tools are selected by analyzing the problem manually, or by using several performance prediction techniques. The manual identification is based on the data properties only, whereas the performance predictors only estimate basic execution metrics without linking them with big data (3Vs) thresholds. Hence, both ways of identification are mostly incorrect, which can lead to inefficient use of 3Vs optimizations, resulting into global inefficiency, reduced system performance, increasing power consumption, requiring greater effort on the part of the programming team, and misallocation of the hardware resources required for the task. In this regard, a novel framework has been proposed for automatic detection of 3Vs (Volume, Velocity, Variety) of big data, using machine learning. The detection is done through static code features, data, and platform properties, leading to relevant tool selection, and code generation, with minimal overheads, lesser programmer interventions, higher usability, and portability. Instead of handling each application with big data specialized solutions, or manually identifying the 3Vs, the framework can automatically detect and link the 3Vs to the relevant optimizations. Several standard applications have been tested using the proposed framework. In the case of volume, the average detection accuracy is up to 97.8% for seen and 95.9% for unseen applications. In the case of velocity, the average detection accuracy is up to 97.3% for seen and 92.6% for unseen applications. There is no margin of error in variety detection, as it has straightforward computations without any predictions. Furthermore, an airline recommendation system case study strengthens the effectiveness of the proposed approach.

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Investigating airline passenger satisfaction: Data mining method

Noviantoro

Huang

2022

Research in Transportation Business & Management

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Airlines Content Recommendations Based on Passengers' Choice Using Bayesian Belief Networks

Cited by 5 publications

References 15 publications

Forecasting the nearly unforecastable: why aren’t airline bookings adhering to the prediction algorithm?

Forecasting the nearly unforecastable: why aren’t airline bookings adhering to the prediction algorithm?

Towards a Novel Framework for Automatic Big Data Detection

Investigating airline passenger satisfaction: Data mining method

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