Heterogeneous Item Recommendation for the Air Travel Industry

  • Zhicheng He
  • Jie LiuEmail author
  • Guanghui Xu
  • Yalou Huang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11440)


Analyzing the travel behaviors and patterns of air passengers have always been of great significance to the air travel industry. Understanding the demands and interests of passengers behind their behaviors is a crucial and fundamental task for many applications. However, this task is challenging due to the lack of customer information, data sparsity, and the long-tail distribution. In this paper, we investigate the problem of heterogeneous item recommendation by learning representations of items and passengers in a shared latent space. Specifically, we first establish a heterogeneous information network (HIN) through statistical analysis, where the edges represent the interactions between different nodes. Each node also contains some auxiliary attribute information that describes its travel behavior or that of its passenger groups. Then we devise a joint matrix factorization model to learn node representations based on the HIN, where both the heterogeneous edges and the node attributes are incorporated into the learning process. Moreover, a weighting strategy is further utilized to deal with the long-tail distribution of passenger behaviors based on the implicit feedback information. Experimental results conducted on a real-world passenger name record (PNR) dataset demonstrate the effectiveness of the proposed method.


Air travel data analysis Matrix factorization Air route recommendation Airline recommendation 



This research is supported by the National Natural Science Foundation of China under grant No. U1633103, Natural Science Foundation of Tianjin under grant No. 18JCYBJC15800, and the Open Project Foundation of Information Technology Research Base of Civil Aviation Administration of China under grant No. CAAC-ITRB-201701.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Zhicheng He
    • 1
  • Jie Liu
    • 2
    Email author
  • Guanghui Xu
    • 1
  • Yalou Huang
    • 3
  1. 1.College of Computer ScienceNankai UniversityTianjinChina
  2. 2.College of Artificial IntelligenceNankai UniversityTianjinChina
  3. 3.College of SoftwareNankai UniversityTianjinChina

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