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Elahe Khazaei and Abbas Alimohammadi
Location-based social networking services have attracted great interest with the growth of smart mobile devices. Recommending locations for users based on their preferences is an important task for location-based social networks (LBSNs). Since human bein...
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Zheng Li, Xueyuan Huang, Chun Liu and Wei Yang
As the core of location-based social networks (LBSNs), the main task of next point-of-interest (POI) recommendation is to predict the next possible POI through the context information from users? historical check-in trajectories. It is well known that sp...
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Álvaro Bernabeu-Bautista, Leticia Serrano-Estrada, V. Raul Perez-Sanchez and Pablo Martí
This research sheds light on the relationship between the presence of location-based social network (LBSN) data and other economic and demographic variables in the city of Valencia (Spain). For that purpose, a comparison is made between location patterns...
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Jiping Liu, Zhiran Zhang, Chunyang Liu, Agen Qiu and Fuhao Zhang
With the rapid development of location-based social networks (LBSNs), because human behaviors exhibit specific distribution patterns, personalized geo-social recommendation has played a significant role for LBSNs. In addition to user preference and socia...
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Mingxin Gan and Ling Gao
Point-of-interest (POI) recommendations in location-based social networks (LBSNs) allow online users to discover various POIs for social activities occurring in the near future close to their current locations. Research has verified that people?s prefere...
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Sadaf Safavi and Mehrdad Jalali
In location-based social networks (LBSNs), exploit several key features of points-of-interest (POIs) and users on precise POI recommendation be significant. In this work, a novel POI recommendation pipeline based on the convolutional neural network named...
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Yan Zhou, Kaixuan Zhou and Shuaixian Chen
The rapid development of big data technology and mobile intelligent devices has led to the development of location-based social networks (LBSNs). To understand users? behavioral patterns and improve the accuracy of location-based services, point-of-inter...
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Shuqiang Xu, Qunying Huang and Zhiqiang Zou
Location-based social networks (LBSN) allow users to socialize with friends by sharing their daily life experiences online. In particular, a large amount of check-ins data generated by LBSNs capture the visit locations of users and open a new line of res...
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Jing Tian, Zilin Zhao and Zhiming Ding
With the widespread use of the location-based social networks (LBSNs), the next point-of-interest (POI) recommendation has become an essential service, which aims to understand the user?s check-in behavior at the current moment by analyzing and mining th...
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Xueying Wang, Yanheng Liu, Xu Zhou, Zhaoqi Leng and Xican Wang
The next point-of-interest (POI) recommendation is one of the most essential applications in location-based social networks (LBSNs). Its main goal is to research the sequential patterns of user check-in activities and then predict a user?s next destinati...
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Hang Zhang, Mingxin Gan and Xi Sun
In location-based social networks (LBSNs), point-of-interest (POI) recommendations facilitate access to information for people by recommending attractive locations they have not previously visited. Check-in data and various contextual factors are widely ...
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Chunyang Liu, Jiping Liu, Jian Wang, Shenghua Xu, Houzeng Han and Yang Chen
Point-of-interest (POI) recommendation is one of the fundamental tasks for location-based social networks (LBSNs). Some existing methods are mostly based on collaborative filtering (CF), Markov chain (MC) and recurrent neural network (RNN). However, it i...
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