7th ACM SIGSPATIAL International Workshop on Location-Based Social Networks (LBSN 2014)
** CALL FOR PAPERS ** November 4, 2014 - Dallas, Texas, USA Held in conjunction with ACM SIGSPATIAL 2014 Website: http://faculty.ce.berkeley.edu/pozdnukhov/lbsn14/index.html *Aims and Scope* Social networks have been prevalent on the Internet and become a hot research topic attracting many professionals from a variety of fields. The advances in location-acquisition and mobile communication technologies empower people to use location data with existing online social networks. The dimension of location helps bridge the gap between the physical world and online social networking services. Furthermore, people in an existing social network can expand their social structure with the new interdependency derived from their locations. As location is one of the most important components of user context, extensive knowledge about an individual’s interests, behaviors, and relationships with others can be learned from her locations. These kinds of location-embedded and location-driven social structures are known as location-based social networks. The objective of this workshop is to provide professionals, researchers, and technologists with a single forum where they can discuss and share the state-of-the-art of LBSN development and applications, present their ideas and contributions, and set future directions in emerging innovative research for location based social networks. *Topics of Interest* Topics of interest include, but not limited to, the following aspects : *Understanding users in LBSNs* - User preference modeling - Location prediction and inference using social network - User mobility modeling and analysis - Real-world user activity sensing and recognition - User similarity computing based on locations - Link prediction and social tiers inference - Friend recommendations and community discovery - Expert discovery and influential person identification - User intention understanding *Understanding locations in LBSNs* - Hot spots, significant places, and interesting locations detection - Generic or personalized location recommendations - Popular travel routes discovery from social media - Trip planning and itinerary suggestion for users - Location annotation and semantic meaning identification - Location prediction and location privacy - Anomaly detection and event discovery from social media - Trajectory data mining in LBSNs - Data structure of location information *Information sharing and Data management in LBSNs* - Location and location-related data sharing - Location and location-tagged media visualization - Human-computer interaction in LBSNs - Information retrieval in LBSNs - Data management in LBSNs - Privacy concerns and best practices in location information sharing Submission We invite two kinds of submissions made via the submission website <https://cmt.research.microsoft.com/LBSN2014/>: - *Full research papers – up to 10 pages (recommended length is 8 pages)* - *Vision papers and short technical papers - up to 4 pages* All manuscripts should be submitted in a single PDF file including all content, figures, tables, and references, following ACM camera-ready templates available at:http://www.acm.org/sigs/pubs/proceed/template.html, via the submission website <https://cmt.research.microsoft.com/LBSN2013/> before the submission deadline. Each paper will be assigned to three reviewers for a peer review. ACM Digital Library One author per accepted workshop paper is required to register for both the main SIGSPATIAL conference and the workshop, to attend the workshop, and to present the accepted paper in the workshop. Otherwise, the accepted paper will not appear in the workshop proceedings or in the ACM Digital Library version of the workshop proceedings. Awards The Best Paper award will be presented according to the review results and the presentation of a paper. Important Dates: *Paper submission:* *September 1, 2014**Notification of acceptance:*October 1, 2014*Camera-ready copies due:* October 8, 2014*Workshop date:*November 4, 2014
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