计算机科学

计算机科学 ›› 2025, Vol. 52 ›› Issue (11A): 250200088-6.doi: 10.11896/jsjkx.250200088

• 数据库&大数据&数据科学 • 上一篇    下一篇

基于图卷积神经网络的多属性个性化航空行程推荐系统

彭明田1, 王味帅2, 田丰1, 李江涛1, 卢燕1, 马淑燕1, 朱红林1, 刘驰2   

  1. 1 中国民航信息网络股份有限公司北京市民航大数据工程技术研究中心 北京 100318
    2 北京理工大学计算机学院 北京 100081
  • 出版日期:2025-11-15 发布日期:2025-11-10
  • 通讯作者: 刘驰(chiliu@bit.edu.cn)
  • 作者简介:mtpeng@travelsky.com.cn
  • 基金资助:
    智能动态行程规划技术研究项目

Personalized Multi-attribute Airline Itinerary Recommendation System by Graph ConvolutionalNeural Network

PENG Mingtian1, WANG Weishuai 2, TIAN Feng1, LI Jiangtao1, LU Yan1, MA Shuyan1, ZHU Honglin1, LIU Chi 2   

  1. 1 Beijing Civil Aviation Big Data Engineering Technology Research Center,Travelsky Technology Limited,Beijing 100318,China
    2 School of Computer Science,Beijing Institute of Technology,Beijing 100081,China
  • Online:2025-11-15 Published:2025-11-10
  • Supported by:
    Intelligent Dynamic Journey Planning Technology Research Project．

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摘要： 航空市场的快速扩展使航班选择愈加复杂,旅客难以从海量信息中选出最佳方案。现有航空行程推荐系统多采用按价格、时间或准点率排序的静态方法,难以兼顾用户个性化需求和多联程航班组合的复杂性。针对这种情况,提出了基于图卷积神经网络的多属性个性化航空行程推荐系统,以图结构数据处理提升推荐精度和个性化效果。该系统构建航班数据的图结构模型,细化航班关键属性,并将用户历史购票行为转化为图节点间的交互信息。通过图卷积神经网络逐层特征聚合,捕捉用户与航班属性间的高阶关系。实验结果表明,该模型有效结合用户偏好与航班静态属性,显著提高了推荐系统的性能与准确性,为用户提供更优的行程建议。

关键词: 图卷积神经网络, 航空行程推荐系统, 个性化推荐, 多联程航班, 用户行为分析

Abstract: The rapid expansion of the aviation market has made flight choices increasingly complex,making it difficult for passengers to choose the best option from a massive amount of information.Existing flight recommendation systems mostly use static methods of sorting by price,time or punctuality,which make it difficult to take into account the personalized needs of users and the complexity of multi-connection flight combinations.In response to this situation,this paper proposes an air itinerary recommendation system based on graph convolutional neural networks,which uses graph structure data processing to improve recommendation accuracy and personalized effects.The system builds a graph structure model of flight data,refines the key attributes of flights,and converts users’ historical ticket purchasing behavior into interactive information between graph nodes.Through layer-by-layer feature aggregation through graph convolutional neural network,the high-order relationship between users and flight attributes is captured.Experiments show that the proposed model effectively combines user preferences and flight static attributes,significantly improves the performance and accuracy of the recommendation system,and provides users with better itinerary suggestions

Key words: Graph convolutional neural network, Air itinerary recommendation system, Personalized recommendation, Multi-connection flights, User behavior analysis

中图分类号: 

  • TP391
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