TY - JOUR
T1 - A Personalized Flight Recommender System Based on Link Prediction in Aviation Data
AU - Kan, Ho Yin
AU - Wong, Dennis
AU - Chau, Keith
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2024
Y1 - 2024
N2 - Providing suitable flight recommendations to passengers is one of the essential requirements for ensuring customer satisfaction and maintaining a strong relationship with them in airline companies. Determining the appropriateness of a flight for a passenger is a complex issue that can depend on various factors. Factors such as individual preferences, flight quality, and the possibility of flight cancellations or delays need to be simultaneously considered in the process of making appropriate flight recommendations. Additionally, the vast amount of flight data adds to the complexity of this issue. In this article, a personalized flight recommender system is presented to address these challenges. The proposed method utilizes a link prediction strategy to model user profiles and habits, limiting the set of feasible recommendations. Furthermore, a Convolutional Neural Network (CNN) is employed to predict the likelihood of flight cancellations or delays, enabling the system to provide passengers with recommendations that maximize their satisfaction based on this information combined with flight features. To reduce the complexity of handling large flight datasets and increase processing speed, the proposed approach utilizes clustering. In this technique, data is distributed into a set of clusters using the K-Means algorithm, and the recommendation process is based on the cluster with the least distance to the user's features. The performance of the proposed method was tested using real flight data. The experiments evaluated the model's accuracy in predicting flight delays/cancellations and the accuracy of the recommendations it provides. The results demonstrated that the CNN model employed in the proposed method achieved an average accuracy of 95.13%in predicting flight delays/cancellations, showing at least a 2.4%improvement over the compared methods. Additionally, the proposed recommender system reported an accuracy value of 72.31%, surpassing the compared works by 15.6%and indicating its favorable performance in providing accurate recommendations.
AB - Providing suitable flight recommendations to passengers is one of the essential requirements for ensuring customer satisfaction and maintaining a strong relationship with them in airline companies. Determining the appropriateness of a flight for a passenger is a complex issue that can depend on various factors. Factors such as individual preferences, flight quality, and the possibility of flight cancellations or delays need to be simultaneously considered in the process of making appropriate flight recommendations. Additionally, the vast amount of flight data adds to the complexity of this issue. In this article, a personalized flight recommender system is presented to address these challenges. The proposed method utilizes a link prediction strategy to model user profiles and habits, limiting the set of feasible recommendations. Furthermore, a Convolutional Neural Network (CNN) is employed to predict the likelihood of flight cancellations or delays, enabling the system to provide passengers with recommendations that maximize their satisfaction based on this information combined with flight features. To reduce the complexity of handling large flight datasets and increase processing speed, the proposed approach utilizes clustering. In this technique, data is distributed into a set of clusters using the K-Means algorithm, and the recommendation process is based on the cluster with the least distance to the user's features. The performance of the proposed method was tested using real flight data. The experiments evaluated the model's accuracy in predicting flight delays/cancellations and the accuracy of the recommendations it provides. The results demonstrated that the CNN model employed in the proposed method achieved an average accuracy of 95.13%in predicting flight delays/cancellations, showing at least a 2.4%improvement over the compared methods. Additionally, the proposed recommender system reported an accuracy value of 72.31%, surpassing the compared works by 15.6%and indicating its favorable performance in providing accurate recommendations.
KW - Aviation data
KW - clustering
KW - convolutional neural network
KW - link prediction
KW - recommender system
UR - http://www.scopus.com/inward/record.url?scp=85186107715&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2024.3369487
DO - 10.1109/ACCESS.2024.3369487
M3 - Article
AN - SCOPUS:85186107715
SN - 2169-3536
VL - 12
SP - 36961
EP - 36973
JO - IEEE Access
JF - IEEE Access
ER -