Partition-based Collaborative Tensor Factorization for POI Recommendation

Wenjing Luan; Guanjun Liu; Changjun Jiang; Liang Qi

doi:10.1109/JAS.2017.7510538

Volume 4 Issue 3

Jul. 2017

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2017 > 4(3): 437-446

Wenjing Luan, Guanjun Liu, Changjun Jiang and Liang Qi, "Partition-based Collaborative Tensor Factorization for POI Recommendation," IEEE/CAA J. Autom. Sinica, vol. 4, no. 3, pp. 437-446, July 2017. doi: 10.1109/JAS.2017.7510538

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Wenjing Luan, Guanjun Liu, Changjun Jiang and Liang Qi, "Partition-based Collaborative Tensor Factorization for POI Recommendation," IEEE/CAA J. Autom. Sinica, vol. 4, no. 3, pp. 437-446, July 2017. doi: 10.1109/JAS.2017.7510538

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Partition-based Collaborative Tensor Factorization for POI Recommendation

doi: 10.1109/JAS.2017.7510538

Wenjing Luan,
Guanjun Liu^{1, 2
,
,},
Changjun Jiang^{2
,
,},
Liang Qi^1
,

1.
Department of Computer Science and Technology, Tongji University, Shanghai 200092, China
2.
Key Laboratory of Embedded System and Service Computing, Tongji University, Shanghai 200092, China

Funds:

in part by the National Nature Science Foundation of China 91218301

in part by the National Nature Science Foundation of China 61572360

the Basic Research Projects of People's Public Security University of China 2016JKF01316

in part by Shanghai Shuguang Program 15SG18

More Information

Author Bio:
Wenjing Luan (S'16) received the B.S. and M.S. degrees from Shandong University of Science and Technology, Qingdao, China, in 2009 and 2012, respectively. She is now pursuing the Ph.D. degree in the Department of Computer Science and Technology, Tongji University, Shanghai, China. She received the Best Student Paper Award-Finalist in the 13th IEEE International Conference on Networking, Sensing and Control (ICNSC 2016) Conference. Her current research interests include location-based social networks, recommender system, and intelligent transportation systems.(e-mail: wenjingmengjing@163.com)

Guanjun Liu (M'16) received the Ph.D. degree in computer software and theory from Tongji University, Shanghai, China, in 2011. He was a postdoctoral research fellow at Singapore University of Technology and Design, Singapore, from 2011 to 2013. He worked at Humboldt-University zu Berlin, Germany, from 2013 to 2014 as a postdoctoral research fellow supported by the Alexander von Humboldt Foundation. He is currently an associate professor with the Department of Computer Science and Technology, Tongji University. He has published 50+ papers. His research interests include Petri net theory, model checking, web service, workflow, discrete event systems, and information security.(e-mail: liugj1116@163.com)

Changjun Jiang received the Ph.D. degree from the Institute of Automation, Chinese Academy of Science, Beijing, China, in 1995. He is currently the leader of the Key Laboratory of the Ministry of Education for Embedded System and Service Computing, Tongji University, Shanghai, China. He is an IET Fellow and an Honorary Professor with Brunel University, London. He has published more than 300 papers in journals and conference proceedings, including Chinese Science, the IEEE Transactions on Robotics and Automation, and the IEEE Transactions on Fuzzy Systems. He has led over 30 projects supported by the National Natural Science Foundation of China, the National High Technology Research and Development Program of China, and the National Basic Research Developing Program of China. His research interests include concurrency theory, Petri nets, formal verification of software, cluster, grid technology, intelligent transportation systems, and service-oriented computing. Prof. Jiang has been the recipient of one international prize and seven domestic prizes in the field of science and technology.(e-mail: cjjiang@tongji.edu.cn)

Liang Qi (S'16) received the B.S. and M.S. degrees from Shandong University of Science and Technology, Qingdao, China, in 2009 and 2012, respectively. He is currently working toward the Ph.D. degree with the Department of Computer Science and Technology, Tongji University, Shanghai, China. He has been supported by a scholarship from the China Scholarship Council. From 2015 to 2016, he was a joint Ph.D. Student with the Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ, USA. He has authored over a dozen technical papers in journals and conference proceedings. His research interests include Petri nets, recommender system, and intelligent transportation systems.(e-mail: 1210513@tongji.edu.cn)
Corresponding author: Guanjun Liu, e-mail:liugj1116@163.com; Guanjun Liu, e-mail:liugj1116@163.com
Received Date: 2016-04-17
Accepted Date: 2016-12-20

Abstract

Abstract

The rapid development of location-based social networks (LBSNs) provides people with an opportunity of better understanding their mobility behavior which enables them to decide their next location. For example, it can help travelers to choose where to go next, or recommend salesmen the most potential places to deliver advertisements or sell products. In this paper, a method for recommending points of interest (POIs) is proposed based on a collaborative tensor factorization (CTF) technique. Firstly, a generalized objective function is constructed for collaboratively factorizing a tensor with several feature matrices. Secondly, a 3-mode tensor is used to model all users' check-in behaviors, and three feature matrices are extracted to characterize the time distribution, category distribution and POI correlation, respectively. Thirdly, each user's preference to a POI at a specific time can be estimated by using CTF. In order to further improve the recommendation accuracy, PCTF (Partitionbased CTF) is proposed to fill the missing entries of a tensor after clustering its every mode. Experiments on a real checkin database show that the proposed method can provide more accurate location recommendation.
- Clustering,
- context,
- feature extraction,
- point of interest (POI) recommendation,
- tensor factorization

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References(36)

References

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Partition-based Collaborative Tensor Factorization for POI Recommendation

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