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Characterizing users' check-in activities using their scores in a location-based social network

Lei Jin, Xuelian Long, Ke Zhang, Yu-Ru Lin, James Joshi
2014 Multimedia Systems  
Analysis of users' check-ins in location-based social networks (LBSNs, also called GeoSocial Networks), such as Foursquare and Yelp, is essential to understand users' mobility patterns and behaviors.  ...  A user's score is an aggregate measure computed by the system based on more accurate and complete check-ins of the user.  ...  Introduction Characterizing users' check-in activities in location-based social networks (LBSNs), such as Foursquare and Yelp, is an important research topic as it can help LBSNs to better understand users  ... 
doi:10.1007/s00530-014-0395-8 fatcat:5trpv3jzerg2fnd4olkvtcuvzy

Towards understanding the gamification upon users' scores in a location-based social network

Lei Jin, Ke Zhang, Jianfeng Lu, Yu-Ru Lin
2014 Multimedia tools and applications  
In this paper, we aim to examine the effectiveness of a social gamification mechanism, named user scores, designed in Foursquare which is one of most popular location-based social networks.  ...  The identified influence on user scores has the important implication on applications including friend and venue recommendations in location-based social networks.  ...  Our work in this paper is to identify the social incentive and influence between friends who engage in a social gamification mechanism provided in a location-based social network (LBSN), Foursquare.  ... 
doi:10.1007/s11042-014-2317-3 fatcat:fx6jtaeh7feolenjxcokp3ln2i

It's the way you check-in

Luca Rossi, Mirco Musolesi
2014 Proceedings of the second edition of the ACM conference on Online social networks - COSN '14  
In recent years, the rapid spread of smartphones has led to the increasing popularity of Location-Based Social Networks (LBSNs).  ...  current location (i.e., their check-ins).  ...  In fact, LBSNs users willingly share their location data on the network, where their identity is publicly visible to all the other users.  ... 
doi:10.1145/2660460.2660485 dblp:conf/cosn/RossiM14 fatcat:tr5x2x2fxrelfjzthh4cgrdway

A sentiment-enhanced personalized location recommendation system

Dingqi Yang, Daqing Zhang, Zhiyong Yu, Zhu Wang
2013 Proceedings of the 24th ACM Conference on Hypertext and Social Media - HT '13  
Using two datasets extracted from the location based social networks Foursquare, experiment results demonstrate that the proposed hybrid preference model can better characterize user preference by maintaining  ...  First, we propose a hybrid user location preference model by combining the preference extracted from check-ins and text-based tips which are processed using sentiment analysis techniques.  ...  Location Based Social MF As mentioned in the problem definition section, based on PMF approach, we design our location based social MF model considering both user social network and venue similarity network  ... 
doi:10.1145/2481492.2481505 dblp:conf/ht/YangZYW13 fatcat:4flxaxpkrveslklulchuuv6rym

User modeling for point-of-interest recommendations in location-based social networks: the state-of-the-art [article]

Shudong Liu
2017 arXiv   pre-print
Location-based social networks(LBSNs)allow users to check-in at a physical location and share daily tips on points-of-interest (POIs) with their friends anytime and anywhere.  ...  , geographical information-based user modeling, spatio-temporal information-based user modeling, and geo-social information-based user modeling.  ...  Location-based social networks (LBSNs) allow users to check-in and share their locations, tips, and experiences about points-of-interest (POIs) with their friends anytime and anywhere.  ... 
arXiv:1712.06768v1 fatcat:nzmsjj6kjzby7ldi3czf6zlkye

Exploiting place features in link prediction on location-based social networks

Salvatore Scellato, Anastasios Noulas, Cecilia Mascolo
2011 Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '11  
In this paper we study the problem of designing a link prediction system for online location-based social networks.  ...  Link prediction systems have been largely adopted to recommend new friends in online social networks using data about social interactions.  ...  Acknowledgments We would like to thank Ilias Leontiadis and Charalampos Rotsos for many useful comments and discussions.  ... 
doi:10.1145/2020408.2020575 dblp:conf/kdd/ScellatoNM11 fatcat:2w2mdmoqrnamhctu4q36os77fa

Discovering and predicting user routines by differential analysis of social network traces

Fabio Pianese, Xueli An, Fahim Kawsar, Hiroki Ishizuka
2013 2013 IEEE 14th International Symposium on "A World of Wireless, Mobile and Multimedia Networks" (WoWMoM)  
We report our findings based on two localized data sets about a single pool of users: the former contains public geotagged Twitter messages, the latter Foursquare check-ins that provide us with meaningful  ...  Instead of actively sampling increasing volumes of sensor data, we explore the participatory sensing potential of multiple mobile social networks, on which users often disclose information about their  ...  In this paper we investigate the following question: "To what extent can location traces provided by users via normal interactions with social networking applications help us understand and characterize  ... 
doi:10.1109/wowmom.2013.6583383 dblp:conf/wowmom/PianeseAKI13 fatcat:767wufx5mjahbh433fm4kgk22m

Measurement and Analysis of the Swarm Social Network With Tens of Millions of Nodes

Yang Chen, Jiyao Hu, Hao Zhao, Yu Xiao, Pan Hui
2018 IEEE Access  
This paper provides a deep analysis of social interactions between Swarm users, and reveals the relationship between social connectivity and check-in activities.  ...  In this paper, we crawl the entire social network of Swarm, a leading mobile social app with more than 60 million users, using a distributed approach.  ...  Swarm focuses on serving mobile users by engaging them in location-centric activities. Swarm users can share their locations through the social network.  ... 
doi:10.1109/access.2018.2789915 fatcat:fedc3alimvhrnbglmqjmjokewq

Location prediction on trajectory data: A review

Ruizhi Wu, Guangchun Luo, Junming Shao, Ling Tian, Chengzong Peng
2018 Big Data Mining and Analytics  
Location prediction is the key technique in many location based services including route navigation, dining location recommendations, and traffic planning and control, to mention a few.  ...  Then, we review existing location-prediction methods, ranging from temporal-pattern-based prediction to spatiotemporal-pattern-based prediction.  ...  Acknowledgment We thank the editors and reviewer for everything you have done for us.  ... 
doi:10.26599/bdma.2018.9020010 dblp:journals/bigdatama/WuLSTP18 fatcat:3ogap5xsxffjxazjm7chcnqu3u

A Neural Network Approach to Joint Modeling Social Networks and Mobile Trajectories [article]

Cheng Yang, Maosong Sun, Wayne Xin Zhao, Zhiyuan Liu, Edward Y.Chang
2017 arXiv   pre-print
A combination of social networking and location-based services is called as location-based social networks (LBSN).  ...  We first adopt a network embedding method for the construction of social networks: a networking representation can be derived for a user.  ...  ACKNOWLEDGMENTS The authors thank the anonymous reviewers for their valuable and constructive comments.  ... 
arXiv:1606.08154v2 fatcat:xinquhxbhnbczie5kcn5zmpppm

Identifying and characterizing user communities on Twitter during crisis events

Aditi Gupta, Anupam Joshi, Ponnurangam Kumaraguru
2012 Proceedings of the 2012 workshop on Data-driven user behavioral modelling and mining from social media - DUBMMSM '12  
First, we defined a similarity metric between users based on their links, content posted and meta-data.  ...  Twitter is a prominent online social media which is used to share information and opinions.  ...  We compute similarity between two users based on the value in their location field. The field is a text box, the user may leave it blank, or fill it with a valid / invalid location.  ... 
doi:10.1145/2390131.2390142 dblp:conf/cikm/0003JK12 fatcat:a223gxkpzjhedczksbmfyha3ue

Finding Potential Propagators and Customers in Location-Based Social Networks: An Embedding-Based Approach

Yi-Chun Chen, Cheng-Te Li
2020 Applied Sciences  
In the scenarios of location-based social networks (LBSN), the goal of location promotion is to find information propagators to promote a specific point-of-interest (POI).  ...  Given a target POI l to be promoted, TPD aims at finding a set of influential users, who can generate more users to visit l in the future, and TCD is to find a set of potential users, who will visit l  ...  Then location-based Social Networks (LBSN) connecting user social relationships and their check-ins at POIs can be generated.  ... 
doi:10.3390/app10228003 fatcat:72a7zjdr4fdvtit2gmwyy2o4ey

The Call of the Crowd: Event Participation in Location-based Social Services [article]

Petko Georgiev, Anastasios Noulas, Cecilia Mascolo
2014 arXiv   pre-print
This paper takes advantage of data from a widely used location-based social network, Foursquare, to analyze event patterns in three metropolitan cities.  ...  We put forward several hypotheses on the motivating factors of user participation and confirm that social aspects play a major role in determining the likelihood of a user to participate in an event.  ...  Users can optionally share their check-ins via their Twitter accounts which enables us to crawl the check-ins via the Twitter streaming API.  ... 
arXiv:1403.7657v1 fatcat:2e4h4xx4t5fv7l2x45a6pzrsza

A Better Semantic based Friend Recommendation System for Modern Social Networks

Akash Bhapkar, Kajal Fegade, Rahul Ahire, Chitra Chaudhary, A. M.
2016 International Journal of Computer Applications  
Upon receiving a request, a list of people with highest recommendation scores is returned to the query user.  ...  The user's data is stored in database and lifestyle is extracted using topic model. By constructing friend-matching graph, our system depicts the similarity of lifestyles between two users.  ...  Existing social networking services recommend friends to users based on their social graphs, which may not be the most appropriate to reflect a user's preferences on friend selection in real life.  ... 
doi:10.5120/ijca2016912475 fatcat:c37cg3qesvdc5mhxjxywp3j4xe

Probabilistic Local Expert Retrieval [chapter]

Wen Li, Carsten Eickhoff, Arjen P. de Vries
2016 Lecture Notes in Computer Science  
This paper proposes a range of probabilistic models of local expertise based on geo-tagged social network streams.  ...  We assume that frequent visits result in greater familiarity with the location in question. To capture this notion, we rely on spatio-temporal information from users' online check-in profiles.  ...  Location-based social networks allow users to post messages and document their whereabouts. When a user checks in at a given location, the action of check-in is not merely a user-place tuple.  ... 
doi:10.1007/978-3-319-30671-1_17 fatcat:3o3woyapuza65aetqvecaswrpi
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