Dynamic discovery of favorite locations in spatio-temporal social networks |
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| Institution: | 1. School of Cybersecurity, Chengdu University of Information Technology, Chengdu 610225, China;2. School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China;3. School of Computer Science and Engineering, Nanyang Technological University, Singapore 639798, Singapore;4. School of Software Engineering, Chengdu University of Information Technology, Chengdu 610225, China;5. West China School of Medicine, Sichuan University, Chengdu 610041, China;6. School of Public Administration, Sichuan University, Chengdu 610065, China;1. Computer Engineering Department, Middle East Technical University, Ankara, Turkey;2. School of Computing Science, University of Glasgow, Glasgow, UK;3. Computer Engineering Department, Bilkent University, Ankara, Turkey;1. The Hong Kong Polytechnic University, Hong Kong, China;2. Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, China;3. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China;4. Bio-Computing Research Center, Harbin Institute of Technology, Shenzhen, China;5. Shenzhen Key Laboratory of Visual Object Detection and Recognition, Shenzhen, China;1. Beijing University of Posts and Telecommunications, Beijing, China;2. Singapore Management University, Singapore;3. Worcester Polytechnic Institute, USA;4. Alibaba Group, Hangzhou, China;1. Laboratory of Bioinformatics and Drug Design, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran;2. Department of Biotechnology, College of Science, University of Tehran, Tehran, Iran |
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| Abstract: | A large volume of data flowing throughout location-based social networks (LBSN) gives support to the recommendation of points-of-interest (POI). One of the major challenges that significantly affects the precision of recommendation is to find dynamic spatio-temporal patterns of visiting behaviors, which can hardly be figured out because of the multiple side factors. To confront this difficulty, we jointly study the effects of users’ social relationships, textual reviews, and POIs’ geographical proximity in order to excavate complex spatio-temporal patterns of visiting behaviors when the data quality is unreliable for location recommendation in spatio-temporal social networks. We craft a novel framework that recommends any user the POIs with effectiveness. The framework contains two significant techniques: (i) a network embedding method is adopted to learn the vectors of users and POIs in an embedding space of low dimension; (ii) a dynamic factor graph model is proposed to model various factors such as the correlation of vectors in the previous phase. A collection of experiments was carried out on two real large-scale datasets, and the experimental outcomes demonstrate the supremacy of the proposed method over the most advanced baseline algorithms owing to its highly effective and efficient performance of POI recommendation. |
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