gSCorr: modeling geo-social correlations for new check-ins on location-based social networks

Location-based social networks (LBSNs) have attracted an increasing number of users in recent years. The availability of geographical and social information of online LBSNs provides an unprecedented opportunity to study the human movement from their socio-spatial behavior, enabling a variety of location-based services. Previous work on LBSNs reported limited improvements from using the social network information for location prediction; as users can check-in at new places, traditional work on location prediction that relies on mining a user's historical trajectories is not designed for this "cold start" problem of predicting new check-ins. In this paper, we propose to utilize the social network information for solving the "cold start" location prediction problem, with a geo-social correlation model to capture social correlations on LBSNs considering social networks and geographical distance. The experimental results on a real-world LBSN demonstrate that our approach properly models the social correlations of a user's new check-ins by considering various correlation strengths and correlation measures.

[1]  Jacob Goldenberg,et al.  Distance Is Not Dead: Social Interaction and Geographical Distance in the Internet Era , 2009, ArXiv.

[2]  Mao Ye,et al.  Location recommendation for location-based social networks , 2010, GIS '10.

[3]  Nguyen Thanh,et al.  A Gaussian Mixture Model for Mobile Location Prediction , 2007, The 9th International Conference on Advanced Communication Technology.

[4]  J. Sempsey.  The death of distance: How the communications revolution will change our lives , 1998 .

[5]  Eric Sun,et al.  Location3: How Users Share and Respond to Location-Based Data on Social , 2011, ICWSM.

[6]  Cecilia Mascolo,et al.  Exploiting place features in link prediction on location-based social networks , 2011, KDD.

[7]  Cecilia Mascolo,et al.  Socio-Spatial Properties of Online Location-Based Social Networks , 2011, ICWSM.

[8]  Ryszard S. Michalski,et al.  Machine learning: an artificial intelligence approach volume III , 1990 .

[9]  Cecilia Mascolo,et al.  Distance Matters: Geo-social Metrics for Online Social Networks , 2010, WOSN.

[10]  Yehuda Koren,et al.  Factorization meets the neighborhood: a multifaceted collaborative filtering model , 2008, KDD.

[11]  Hussein Dia,et al.  An object-oriented neural network approach to short-term traffic forecasting , 2001, Eur. J. Oper. Res..

[12]  Eric Sun,et al.  Location 3 : How Users Share and Respond to Location-Based Data on Social Networking Sites , 2011 .

[13]  John R. Anderson,et al.  MACHINE LEARNING An Artificial Intelligence Approach , 2009 .

[14]  Cecilia Mascolo,et al.  An Empirical Study of Geographic User Activity Patterns in Foursquare , 2011, ICWSM.

[15]  Fabio Porto,et al.  A conceptual view on trajectories , 2008, Data Knowl. Eng..

[16]  Ravi Kumar,et al.  Influence and correlation in social networks , 2008, KDD.

[17]  Cecilia Mascolo,et al.  NextPlace: A Spatio-temporal Prediction Framework for Pervasive Systems , 2011, Pervasive.

[18]  B. Wellman,et al.  Does Distance Matter in the Age of the Internet? , 2008 .

[19]  Huiji Gao,et al.  Harnessing the Crowdsourcing Power of Social Media for Disaster Relief , 2011, IEEE Intelligent Systems.

[20]  Mao Ye,et al.  Exploiting geographical influence for collaborative point-of-interest recommendation , 2011, SIGIR.

[21]  Frances Cairncross.  The death of distance : how the communications revolution will change our lives , 1997 .

[22]  Stuart J. Barnes,et al.  Mobile marketing: the role of permission and acceptance , 2004, Int. J. Mob. Commun..

[23]  Kyumin Lee,et al.  Exploring Millions of Footprints in Location Sharing Services , 2011, ICWSM.

[24]  Lars Backstrom,et al.  Find me if you can: improving geographical prediction with social and spatial proximity , 2010, WWW '10.

[25]  Michael F. Goodchild,et al.  Please Scroll down for Article International Journal of Digital Earth Crowdsourcing Geographic Information for Disaster Response: a Research Frontier Crowdsourcing Geographic Information for Disaster Response: a Research Frontier , 2022 .

[26]  Fei Wang,et al.  The importance of spatial-temporal issues for case-based reasoning in disaster management , 2010, 2010 18th International Conference on Geoinformatics.

[27]  Huan Liu,et al.  Exploring Social-Historical Ties on Location-Based Social Networks , 2012, ICWSM.

[28]  Tu Minh Phuong,et al.  A Gaussian Mixture Model for Mobile Location Prediction , 2007, 2007 IEEE International Conference on Research, Innovation and Vision for the Future.

[29]  Jure Leskovec,et al.  Friendship and mobility: user movement in location-based social networks , 2011, KDD.

[30]  Stephen J. Wright,et al.  Numerical Optimization , 2018, Fundamental Statistical Inference.

[31]  Ravi Jain,et al.  Evaluating location predictors with extensive Wi-Fi mobility data , 2004, INFOCOM.

[32]  Anna Monreale,et al.  WhereNext: a location predictor on trajectory pattern mining , 2009, KDD.

[33]  Michel Bierlaire,et al.  DynaMIT: a simulation-based system for traffic prediction and guidance generation , 1998 .