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"ACM SIGCHI General Interest Announcements (Mailing List)" <[log in to unmask]>
Date:
Mon, 14 May 2012 11:11:21 -0400
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Jason Hong <[log in to unmask]>
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Jason Hong <[log in to unmask]>
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This workshop is being held in conjunction with Ubicomp 2012
in Pittsburgh, PA. The paper submission deadline is June 12, 2012.

http://lbsn2012.cmuchimps.org/

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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 individualís 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, formally defined as follows:

A location-based social network (LBSN) does not only mean adding a 
location to an existing social network so that people in the social 
structure can share location-embedded information, but also consists of 
the new social structure made up of individuals connected by the 
interdependency derived from their locations in the physical world as 
well as their location-tagged media content, such as photos, video, and 
texts. Here, the physical location consists of the instant location of 
an individual at a given timestamp and the location history that an 
individual has accumulated in a certain period.

Further, the interdependency includes not only that two persons co-occur 
in the same physical location or share similar location histories but 
also the knowledge, e.g., common interests, behavior, and activities, 
inferred from an individualís location (history) and location-tagged data.

In a location-based social network, people can not only track and share 
the location-related information of an individual via either mobile 
devices or desktop computers, but also leverage collaborative social 
knowledge learned from user-generated and location-related content, such 
as GPS trajectories and geo-tagged photos. Consequently, LBSNs enable 
many novel applications that change the way we live, such as travel 
planning, location recommendations, friend suggestion, and community 
discovery, while offering many new research opportunities to the 
Ubiquitous computing community, including link prediction, human 
mobility modeling, and user activity recognition, privacy, and computer 
human interaction. Example papers can be found on
   http://research.microsoft.com/en-us/projects/lbsn/default.aspx.

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
- 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

Information sharing in LBSNs
- Location and location-related data sharing
- Location and location-tagged media visualization
- Human-computer interaction in LBSNs
- Information retrieval in LBSNs.


-- 
Jason I. Hong, Assoc. Prof.             http://www.cs.cmu.edu/~jasonh
HCI Institute, School of Computer Science, Carnegie Mellon University

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