|
|
|
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...
ver más
|
|
|
|
|
|
|
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...
ver más
|
|
|
|
|
|
|
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...
ver más
|
|
|
|
|
|
|
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...
ver más
|
|
|
|
|
|
|
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...
ver más
|
|
|
|
|
|
|
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...
ver más
|
|
|
|
|
|
|
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...
ver más
|
|
|
|
|
|
|
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 ...
ver más
|
|
|
|
|
|
|
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...
ver más
|
|
|
|
|
|
|
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...
ver más
|
|
|
|