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Lived experiences of older adults and caregivers on social networks, social support from Chengalpattu, Tamil Nadu: a qualitative study using the Convoy Model of Social Relations. [PDF]

BMC Geriatr
Sivakumar SP, Palanisamy B, Sree S.
europepmc   +1 more source

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Recommendations in location-based social networks: a survey

GeoInformatica, 2015
Recent advances in localization techniques have fundamentally enhanced social networking services, allowing users to share their locations and location-related contents, such as geo-tagged photos and notes. We refer to these social networks as location-based social networks (LBSNs).
Jie Bao, Mohamed Mokbel
exaly   +2 more sources

Location Influence in Location-based Social Networks

Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, 2017
Location-based social networks (LBSN) are social networks complemented with location data such as geo-tagged activ- ity data of its users. In this paper, we study how users of a LBSN are navigating between locations and based on this information we select the most influential locations.
Muhammad Aamir Saleem, Rohit Kumar 0002, Toon Calders, Xike Xie, Torben Bach Pedersen   +4 more
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Location recommendation for location-based social networks

Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems, 2010
In this paper, we study the research issues in realizing location recommendation services for large-scale location-based social networks, by exploiting the social and geographical characteristics of users and locations/places. Through our analysis on a dataset collected from Foursquare, a popular location-based social networking system, we observe that
Mao Ye 0002, Peifeng Yin, Wang-Chien Lee
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Behavior-based location recommendation on location-based social networks

GeoInformatica, 2017
Location recommendation makes suggestions of nearby locations based on user’s locational preferences and spatial movement patterns. In this paper, we propose two novel location recommendation methods called Behavior Factorization (BF) and Latent Behavior Analysis (LBA).
Seyyed Mohammadreza Rahimi, Xin Wang 0004, Behrouz Homayoun Far   +2 more
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Analysis of a Location-Based Social Network

2009 International Conference on Computational Science and Engineering, 2009
Location-based Social Networks (LSNs) allow users to see where their friends are, to search location-tagged contentwithin their social graph, and to meet others nearby. The recent availability of open mobile platforms, such as Apple iPhones and Google Android phones, makes LSNs much more accessible to mobile users.To study how users share their ...
Nan Li, Guanling Chen
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Personalized location recommendation on location-based social networks

Proceedings of the 8th ACM Conference on Recommender systems, 2014
Personalized location recommendation is a special topic of recommendation. It is related to human mobile behavior in the real world regarding various contexts including spatial, temporal, social, and content. The development of this topic is subject to the availability of human mobile data.
Huiji Gao, Jiliang Tang, Huan Liu 0001
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Exploring Social Influence on Location-Based Social Networks

2014 IEEE International Conference on Data Mining, 2014
Recently, with the advent of location-based social networking services (LBSNs), travel planning and location-aware information recommendation based on LBSNs have attracted much research attention. In this paper, we study the impact of social relations hidden in LBSNs, i.e., The social influence of friends.
Yu Ting Wen, Po-Ruey Lei, Wen-Chih Peng, Xiaofang Zhou 0001   +3 more
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Personalized location recommendation for location-based social networks

2017 IEEE/CIC International Conference on Communications in China (ICCC), 2017
With the development of social networks and wireless communication technology, location-based social networks (LBSNs) are developing rapidly. Personalized location service in location-based social networks can provide users with a new point-of-interest (POI). Compared to traditional recommendation, point-of-interest recommendation integrates the social
Qianfang Xu, Jiachun Wang, Bo Xiao 0006
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