Proceedings ArticleDOI
Investigating City Characteristics Based on Community Profiling in LBSNs
Zhu Wang,Daqing Zhang,Dingqi Yang,Zhiyong Yu,Xingshe Zhou,Zhiwen Yu +5 more
- pp 578-585
TLDR
Based on the user-venue check-in relationship and user/venue attributes, and based on the rich metadata of users and venues, a quantitative community profiling mechanism is put forward to indicate the preferences, interests and habits of a community.Abstract:
While 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. Thereby, a systematic community profiling mechanism is needed. With the recent surge of location-based social networks (LBSNs, e.g., Foursquare, Facebook Places), huge amount of digital footprints about users' locations, profiles as well as their online social connections provide sufficient metadata for community profiling. Different from social networks (e.g., Flickr, Facebook) which have explicit groups for users to subscribe or join, LBSNs usually have no explicit community structure. In order to capitalize on the large number of potential users, quality community detection and profiling approaches are needed so as to enable applications such as direct marketing, group tracking, etc. In this paper, based on the user-venue check-in relationship and user/venue attributes, we come out with a novel community profiling framework. Specifically, we first adopt edge-clustering to simultaneously group both users and venues into communities, and then based on the rich metadata of users and venues we put forward a quantitative community profiling mechanism to indicate the preferences, interests and habits of a community. The efficacy of our approach is validated by intensive empirical evaluations using the collected Foursquare dataset of 266,838 users with 9,803,764 check-ins over 2,477,122 venues worldwide.read more
Citations
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Journal ArticleDOI
Participatory Cultural Mapping Based on Collective Behavior Data in Location-Based Social Networks
TL;DR: This article proposes a participatory cultural mapping approach based on collective behavior in LBSNs, and shows that the approach can subtly capture cultural features and generate representative cultural maps that correspond well with traditional cultural maps based on survey data.
Patent
Inferring user preferences from an internet based social interactive construct
TL;DR: In this paper, the authors described improved capabilities for a computer program product embodied in a computer readable medium that, when executing on one or more computers, helps determine an unknown user's preferences through the use of internet based social interactive graphical representations on a computer facility by performing the steps of ascertaining preferences of a plurality of users who are part of an internet-based social interactive construct.
Patent
Interestingness recommendations in a computing advice facility
TL;DR: In this paper, the authors provide a recommendation to a user through a computer-based advice facility, comprising collecting topical information, filtering the collected topical information based on the interestingness aspect, determining an interestingness rating, and providing a user with the recommendation related to the topical information.
Journal ArticleDOI
Cross-domain community detection in heterogeneous social networks
TL;DR: By exploring the heterogenous digital footprints of LBSNs users in the cyber-physical space, this paper comes out with a novel edge-centric co-clustering framework to discover overlapping communities and is able to group like-minded users from different social perspectives.
Dissertation
Understanding Human Dynamics from Large-Scale Location-Centric Social Media Data: Analysis and Applications
TL;DR: Based on large-scale location centric social media data, user preference on POIs with different granularities and its applications in personalized location based services, as well as the spatial-temporal regularity of user activities are studied.
References
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Journal ArticleDOI
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