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Location Influence in Location-based Social Networks
Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, 2017Location-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 +4 more
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Recommendations in location-based social networks: a survey
GeoInformatica, 2015Recent 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 0003 +3 more
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Urban Computing Leveraging Location-Based Social Network Data
ACM Computing Surveys, 2019Urban computing is an emerging area of investigation in which researchers study cities using digital data. Location-Based Social Networks (LBSNs) generate one specific type of digital data that offers unprecedented geographic and temporal resolutions. We
Thiago H. Silva +6 more
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Location-Based Social Networks
2014Location-based Social Networks (LBSNs) can be considered as a special Online Social Network (OSN) category. Actually, an LBSN has the same OSN’s properties, but considers location as the core object of its structure. This chapter initially provides some definitions and basic services that are offered by LBSNs, a brief literature review, and two ...
Wang-Chien Lee, Mao Ye
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Venue attacks in location-based social networks
Proceedings of the 1st ACM SIGSPATIAL International Workshop on Privacy in Geographic Information Collection and Analysis, 2014Location-Based Social Networks (LBSNs), such as Foursquare, Yelp and Facebook Place, have attracted many people, including business owners who use LBSNs to promote their businesses. A physical location is called a venue or a place of interest in an LBSN.
Lei Jin 0003, Hassan Takabi
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Locations recommendation based on check-in data from Location-Based Social Network
GEOINFORMATICS, 2014 In recent years, together with the universal use of GPS embedded mobile phones and popularity of social network, Location-Based Social Network (LBSN) has been a hit, and user volume rises continuously.
Dan Jiang +5 more
semanticscholar +2 more sources
Location-Based Influence Maximization in Social Networks
Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, 2015In this paper, we aim at the product promotion in O2O model and carry out the research of location-based influence maximization on the platform of LBSN. As offline consuming behavior exists under the O2O environment, the traditional online influence diffusion model could not describe the product acceptance accurately.
Tao Zhou +5 more
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Workload characterization of a location-based social network
Social Network Analysis and Mining, 2014Recently, there has been a large popularization of location-based social networks, such as Foursquare and Apontador, in which users can share their current locations, upload tips and make comments about places. Part of this popularity is due to facility access to the Internet through mobile devices with GPS.
Theo Lins +2 more
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Location-Based Social Networks
2012The ability to gather and manipulate real world contextual data, such as user location, in modern software systems presents opportunities for new and exciting application areas. A key focus among those working in the area of Location-Based services today has been the creation of social networks which allow mobile device users to exchange details of ...
Declan Traynor, Kevin Curran
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Location recommendation for location-based social networks
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems, 2010In 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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