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Junfeng TIAN and Zhengqi HOU, “Friendship Inference Based on Interest Trajectory Similarity and Co-occurrence,” Chinese Journal of Electronics, vol. 33, no. 3, pp. 1–13, 2024 doi: 10.23919/cje.2022.00.363
Citation: Junfeng TIAN and Zhengqi HOU, “Friendship Inference Based on Interest Trajectory Similarity and Co-occurrence,” Chinese Journal of Electronics, vol. 33, no. 3, pp. 1–13, 2024 doi: 10.23919/cje.2022.00.363

Friendship Inference Based on Interest Trajectory Similarity and Co-occurrence

doi: 10.23919/cje.2022.00.363
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  • Author Bio:

    Junfeng TIAN graduated from Hebei University in 1986, received a M.S. degree in computer science from Xi’an University of Electronic Science and Technology in 1990- 1991 and M.S. degree in 1995. In 2004, he graduated from the University of Science and Technology of China with a major in computer science and technology. He has been engaged in teaching and research in the fields of distributed computing and network technology for many years. Currently, he is a Professor, and Deputy Secretary General of Open System Specialized Committee of China Computer Federation; Young and Middle-aged Specialist with Outstanding Contribution in Hebei Province; Young and Middle-aged Teacher with Outstanding Contribution in Hebei Province. (Email:tjf@hbu.edu.cn)

    Zhengqi HOU received the B.E. degree in information security from School of Cyberspace Security and Computer, Hebei University, China, in 2020, and is currently studying for the M.S. degree in cyberspace security and computer at Hebei University. His main research interests include information security and social network location privacy security. (Email: hozi_nl@hotmail.com)

  • Corresponding author: Email: hozi_nl@hotmail.com
  • Received Date: 2022-10-27
  • Accepted Date: 2023-03-14
  • Available Online: 2023-07-12
  • Most of the current research on user friendship speculation in location-based social networks is based on the co-occurrence characteristics of users, however, statistics find that co-occurrence is not common among all users; meanwhile, most of the existing work focuses on mining more features to improve the accuracy but ignoring the time complexity in practical applications. On this basis, a friendship inference model named ITSIC is proposed based on the similarity of user interest tracks and joint user location co-occurrence. By utilizing MeanShift clustering algorithm, ITSIC clustered and filtered user check-ins and divided the dataset into interesting, abnormal, and noise check-ins. User interest trajectories were constructed from user interest check-in data, which allows ITSIC to work efficiently even for users without co-occurrences. At the same time, by application of clustering, the single-moment multi-interest trajectory was further proposed, which increased the richness of the meaning of the trajectory moment. The extensive experiments on two real online social network datasets show that ITSIC outperforms existing methods in terms of AUC score and time efficiency compared to existing methods.
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