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论文类型：期刊论文

发表时间：2020-04-01

发表刊物：IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS

收录刊物：EI、SCIE

卷号：16

期号：4

页面范围：2583-2591

ISSN号：1551-3203

关键字：Vehicular ad hoc networks; Public transportation; Vehicles; Trajectory; Uncertainty; Informatics; Data mining; Internet of Things (IoT); network representation learning; trajectory data mining; vehicular ad hoc network

摘要：With the advancement of Internet of Things technology, we are able to collect massive people & x0027;s trajectory data from various GPS services. These large amounts of trajectory records enable us to better understand human mobility patterns. Meanwhile, we are able to extract social relationships based on these digital records to provide personalized recommendation services, such as points of interests (POI) recommendation and friend recommendation. In this paper, we propose to recommend friends for taxi drivers based on vehicular trajectory records. For this purpose, we propose to construct a vehicular ad hoc network based on co-occurrence phenomenon. Furthermore, we take advantages of the network representation learning technique on the vehicular ad hoc network for learning driver vectors. Finally, potential friends are recommended based on the similarity of driver vectors. Extensive experimental results on two real-world datasets demonstrate that our proposed method has the best performance on friend recommendation compared with several state-of-the-art methods. To the best of our knowledge, this is the first attempt to recommend friends for taxi drivers based on vehicular ad hoc network representation learning.