›› 2015,Vol. 30 ›› Issue (5): 1141-1153.doi: 10.1007/s11390-015-1589-z

所属专题： Artificial Intelligence and Pattern Recognition； Data Management and Data Mining

• Special Section on Selected Paper from NPC 2011 • 上一篇    下一篇

旅客社交网络中的家庭团体发现研究

Huai-Yu Wan1(万怀宇), Member, CCF, Zhi-Wei Wang1(王志伟) You-Fang Lin1(林友芳), Xu-Guang Jia2(贾旭光), Yuan-Wei Zhou2(周元炜)   

  1. 1 Beijing Key Laboratory of Traffic Data Analysis and Mining, School of Computer and Information Technology Beijing Jiaotong University, Beijing 100044, China;
    2 TravelSky Technology Limited, Beijing 100010, China
  • 收稿日期:2014-09-16 修回日期:2015-06-12 出版日期:2015-09-05 发布日期:2015-09-05
  • 作者简介:Huai-Yu Wan received his Ph.D. degree in computer science and technology from Beijing Jiaotong University, Beijing, in 2012. He is an assistant professor with the School of Computer and Information Technology, Beijing Jiaotong University. His research interests focus on data mining, social network analysis, and recommender systems.
  • 基金资助:

    This work was partially supported by the Fundamental Research Funds for the Central Universities of China, the National Natural Science Foundation of China under Grant No. 61403023, the Beijing Committee of Science and Technology under Grant No. Z131110002813118, and the Program for Changjiang Scholars and Innovative Research Team in University of China under Grant No. IRT201206.

Discovering Family Groups in Passenger Social Networks

Huai-Yu Wan1(万怀宇), Member, CCF, Zhi-Wei Wang1(王志伟) You-Fang Lin1(林友芳), Xu-Guang Jia2(贾旭光), Yuan-Wei Zhou2(周元炜)   

  1. 1 Beijing Key Laboratory of Traffic Data Analysis and Mining, School of Computer and Information Technology Beijing Jiaotong University, Beijing 100044, China;
    2 TravelSky Technology Limited, Beijing 100010, China
  • Received:2014-09-16 Revised:2015-06-12 Online:2015-09-05 Published:2015-09-05
  • About author:Huai-Yu Wan received his Ph.D. degree in computer science and technology from Beijing Jiaotong University, Beijing, in 2012. He is an assistant professor with the School of Computer and Information Technology, Beijing Jiaotong University. His research interests focus on data mining, social network analysis, and recommender systems.
  • Supported by:

    This work was partially supported by the Fundamental Research Funds for the Central Universities of China, the National Natural Science Foundation of China under Grant No. 61403023, the Beijing Committee of Science and Technology under Grant No. Z131110002813118, and the Program for Changjiang Scholars and Innovative Research Team in University of China under Grant No. IRT201206.

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人们在出行时, 经常与他人结伴而行, 比如跟家人一起去探亲、跟同事一起商务出差、跟朋友一起旅游观光等。家庭团体是客运市场领域最为最常见的一种消费单元。精确地发现家庭团体, 将有助于客运商为旅客提供个性化的出行服务和精准的产品营销。研究了民航客运领域的家庭团体发现问题, 提出了一种基于旅客社交网络的家庭团体发现方法。首先, 从旅客的历史出行记录中抽取他们的共同出行行为, 并构建旅客社交网络;然后, 采用一种协同分类算法将旅客社交关系分为家庭和非家庭关系;最后, 将家庭关系结果作为关系的权值, 并采用带权社区发现算法来发现家庭团体。基于民航领域真实旅客出行记录数据集的实验证明, 提出的方法可以有效地从历史出行记录中发现家庭团体。

Abstract: People usually travel together with others in groups for different purposes, such as family members for visiting relatives, colleagues for business, friends for sightseeing and so on. Especially, the family groups, as a kind of the most common consumer units, have a considerable scale in the field of passenger transportation market. Accurately identifying family groups can help the carriers to provide passengers with personalized travel services and precise product recommendation. This paper studies the problem of finding family groups in the field of civil aviation and proposes a family group detection method based on passenger social networks. First of all, we construct passenger social networks based on their co-travel behaviors extracted from the historical travel records; secondly, we use a collective classification algorithm to classify the social relationships between passengers into family or non-family relationship groups; finally, we employ a weighted community detection algorithm to find family groups, which takes the relationship classification results as the weights of edges. Experimental results on a real dataset of passenger travel records in the field of civil aviation demonstrate that our method can effectively find family groups from historical travel records.

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