Abstract
Personalized recommendation is needed for online flight booking service because it is a difficult task for a traveller to select the flight when the number of available flights is large. Traditionally, we can recommend flights to a user based on his historical orders collected from his account. However, people sometimes book tickets for his family members, friends or colleagues through his account. In this case, the preferences of other travellers should also be considered. Unfortunately, before placing the order, people will not provide passengers’ information. Therefore, we propose a probabilistic method for passenger prediction based on historical behaviors and contextual knowledge. We then experimentally demonstrate its effectiveness on a real dataset. The result shows that our method outperforms conventional methods.
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Acknowledgments
This work is partially supported by China National Science Foundation (Granted Number 61272438, 61472253), Research Funds of Science and Technology Commission of Shanghai Municipality (Granted Number 15411952502, 14511107702) and Cross Research Fund of Biomedical Engineering of Shanghai Jiaotong University (YG2015MS61).
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Zhao, Y., Cao, J., Tan, Y. (2016). Passenger Prediction in Shared Accounts for Flight Service Recommendation. In: Wang, G., Han, Y., Martínez Pérez, G. (eds) Advances in Services Computing. APSCC 2016. Lecture Notes in Computer Science(), vol 10065. Springer, Cham. https://doi.org/10.1007/978-3-319-49178-3_12
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DOI: https://doi.org/10.1007/978-3-319-49178-3_12
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