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Investigating City Characteristics Based on Community Profiling in LBSNs
2012 Second International Conference on Cloud and Green Computing, 2012While the detection of social subgroups (i.e., communities) has always been a fundamental task in social network analysis, few efforts has been made to characterize the detected community. Meanwhile, to effectively facilitate applications based on the community structure, it is very important to understand the features of each community.
Zhu Wang 0001 +5 more
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Design and Implementation of LBSNS Service Model
2012Recently, Location Based Service (LBS) is expanding its service areas with the spread of smart phones and is offering more personalized contents according to the variety of needs from customers. Specially, Location Based Social Network Service (LBSNS) is emerging as the most promising service among the applications of LBS.
Youngdo Joo, Younghwa An
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A Personalized Geographic-Based Diffusion Model for Location Recommendations in LBSN
2014 9th Latin American Web Congress, 2014Location Based Social Networks (LBSN) have emerged with the purpose of allowing users to share their visited locations with their friends. Foursquare, for instance, is a popular LBSN where users endorse and share tips about visited locations. In order to improve the experience of LBSN users, simple recommender services, typically based on geographical ...
Iury Nunes, Leandro Balby Marinho
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On the Impact of Neighborhood Selection Strategies for Recommender Systems in LBSNs
2017Location-based social networks (LBSNs) have emerged as a new concept in online social media, due to the widespread adoption of mobile devices and location-based services. LBSNs leverage technologies such as GPS, Web 2.0 and smartphones to allow users to share their locations (check-ins), search for places of interest or POIs (Point of Interest), look ...
Carlos Rios +2 more
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A Graph-Based Taxonomy of Recommendation Algorithms and Systems in LBSNs
IEEE Transactions on Knowledge and Data Engineering, 2016Recently, location-based social networks (LBSNs) gave the opportunity to users to share geo-tagged information along with photos, videos, and SMSs. Recommender systems can exploit this geographic information to provide much more accurate and reliable recommendations to users.
Pavlos Kefalas +2 more
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Detecting Overlapping Communities in LBSNs with Enhanced Location Privacy
Proceedings of the Third International Symposium on Women in Computing and Informatics, 2015Location based social network (LBSNs) for instance Facebook places and Twitter provides large amount of data which allows service providers to create several applications like group marketing, friend and location recommendations, trend inquiry etc. Location based social networks does not provide precise communities which enables users to subscribe ...
K. Sreelekshmi, Pretty Babu
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Detecting overlapping communities in LBSNs by fuzzy subtractive clustering
Social Network Analysis and Mining, 2018With 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 +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), 2015In 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 +3 more
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Community Detection and Location Recommendation Based on LBSN
2017 International Conference on Network and Information Systems for Computers (ICNISC), 2017Community detection is an effective tool for mining hidden information in social networks. Label propagation is a widely used and effective community detection algorithm. A lot of work has been done based on label propagation for standalone machine computing. While in location based social networks (LBSN), paralleled label propagation is needed to deal
Chang Su
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Personalized POI Recommendation Model in LBSNs
2017The 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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