Results 181 to 190 of about 687 (209)

LocRec: Rule-Based Successive Location Recommendation in LBSN

2018 IEEE International Conference on Communications (ICC), 2018
Successive location recommendation has recently emerged as an important service in Location-Based Social Networks (LBSNs). It aims at recommending the next location(s) to visit to a user given its current and previous locations. Although several recommenders have been proposed, only few works have considered the sequential correlations among locations ...
Hanane Amirat, Abderrahim Benslimane, Philippe Fournier-Viger, Nasreddine Lagraa   +3 more
openaire   +1 more source

Multi-task Learning of Heterogeneous Hypergraph Representations in LBSNs

Location-based service networks (LBSNs) have emerged as a primary source for numerous applications that attempt to understand human mobility and analyze social networks. However, mainstream studies on representation learning often consider LBSNs to be either regular graphs or a mixture of regular graphs and hypergraphs.
Dong Duc Anh Nguyen, Minh Hieu Nguyen 0003, Phi Le Nguyen, Jun Jo 0001, Hongzhi Yin, Thanh Tam Nguyen   +5 more
openaire   +2 more sources

Feature tendency based location prediction in LBSNs

2016 18th International Conference on Advanced Communication Technology (ICACT), 2016
The development of location-based social networks (LBSNs) has brought in massive users' mobility data, providing an unprecedented opportunity to study human mobile behavior. However, the existing location prediction methods suffer from incompleteness of mobility data and disability of selecting the effective feature.
Zi Xing, Hui Tian, Tu Chen, Jing Zhang
openaire   +1 more source

Genetic Location-Based Social Networks (G-LBSN)

Proceedings of the 3rd International Workshop on Location and the Web, 2010
Despite much advances in both general and targeted Social Network Services (SNS) and Location-Based Social Networks (LBSN), there is currently a void in literatures on SNS that form temporary social networks to address specific problems and employ intelligent classification of members and coordination of tasks toward goal oriented action.
openaire   +1 more source

LBSN Data and the Social Butterfly Effect (Vision Paper)

Proceedings of the 8th ACM SIGSPATIAL International Workshop on Location-Based Social Networks, 2015
LBSN data are well-suited for research questions and perspectives on social or spatial phenomena. Researchers often subset large LBSN datasets into different social networks (using snowball sampling), temporal or spatial granularities, to test for statistical patterns.
openaire   +1 more source

A Spatial-Temporal Topic Model for the Semantic Annotation of POIs in LBSNs

ACM Transactions on Intelligent Systems and Technology, 2016
Semantic tags of points of interest (POIs) are a crucial prerequisite for location search, recommendation services, and data cleaning. However, most POIs in location-based social networks (LBSNs) are either tag-missing or tag-incomplete. This article aims to develop semantic annotation techniques to automatically infer tags for POIs.
Tieke He, Hongzhi Yin, Zhenyu Chen 0001, Xiaofang Zhou 0001, Shazia Sadiq, Bin Luo 0003   +5 more
openaire   +4 more sources

NationTelescope: Monitoring and visualizing large-scale collective behavior in LBSNs

Journal of Network and Computer Applications, 2015
The research of collective behavior has attracted a lot of attention in recent years, which can empower various applications, such as recommendation systems and intelligent transportation systems. However, in traditional social science, it is practically difficult to collect large-scale user behavior data.
Yang, Dingqi, Zhang, Daqing, Chen, Longbiao, Qu, Bingqing   +3 more
openaire   +2 more sources

LBSN 2011 Workshop Report: the Third ACM SIGSPATIAL International Workshop on Location-Based Social Networks (LBSN 2011)

SIGSPATIAL Special, 2012
Social networks have been prevalent on the Internet, attracting many professionals from a variety of fields. By adding a location dimension, we can bring online social networks back to the physical world and share our real-life experiences in the virtual world conveniently.
Yu Zheng 0004, Mohamed F. Mokbel
openaire   +1 more source

Friendship Prediction Based on the Fusion of Topology and Geographical Features in LBSN

2013 IEEE 10th International Conference on High Performance Computing and Communications & 2013 IEEE International Conference on Embedded and Ubiquitous Computing, 2013
Friendship prediction in social networks is useful for various applications, such as friend/place recommendation and privacy management. In this paper, we propose a friendship prediction approach by fusing the topology and geographical features in location based social networks (LBSNs). We investigate the features of users' relationship both online and
Hui Luo, Bin Guo 0001, Zhiwen Yu 0001, Zhu Wang 0001, Yun Feng   +4 more
openaire   +1 more source

Continuous Geo-Social Group Monitoring in Dynamic LBSNs

IEEE Transactions on Knowledge and Data Engineering, 2022
Huaijie Zhu, Wei Liu 0061, Jian Yin 0001, Libin Zheng 0001, Xin Huang 0001, Jianliang Xu, Wang-Chien Lee   +6 more
openaire   +1 more source