Results 141 to 150 of about 283 (172)

Personalized POI Recommendation Model in LBSNs

2017
The development of location-based social networks (LBSNs) generates large volume of check-in data. Point-of-interest recommendation (POI) is important for users to find some attractive venues, sometimes when users are in some places far away from their living cities.
Zhong Guo, Ma Changyi
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Detecting overlapping communities in LBSNs by fuzzy subtractive clustering

Social Network Analysis and Mining, 2018
With the increasing popularity of location-based social networks (LBSNs), community detection has emerged as an important and practical issue. One of the main shortcomings of the previous methods is that cluster’s centers have been selected randomly in clustering the communities; therefore, different results are obtained in each execution.
Mohammad Ghane'i-Ostad, Hamed Vahdat-Nejad, Majid Abdolrazzagh-Nezhad   +2 more
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A Friend Recommendation Algorithm Based on Multiple Factors in LBSNs

2015 12th Web Information System and Application Conference (WISA), 2015
In location-based social networks, the current friend recommendation algorithms just take a relatively single factor into account without comprehensive evaluations. To solve this problem, we design a framework - Multiple Heterogeneous Social Network (MHSN) according to users' profiles, check-in records and interests. Based on this framework, we propose
Tiancheng Zhang 0001, Wei Wang 0011, Dejun Yue, Ge Yu 0001   +3 more
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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
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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
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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.
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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
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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 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.
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