Towards Diversified Local Users Identification Using Location Based Social Networks

Identifying a set of diversified users who are local residents in a city is an important task for a wide spectrum of applications such as target ads of local business, surveys and interviews, and personalized recommendations. While many previous studies have investigated the problem of identifying t...

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Vydané v:Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 s. 115 - 118
Hlavní autori: Huang, Chao, Wang, Dong, Zhu, Shenglong
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: New York, NY, USA ACM 31.07.2017
Edícia:ACM Conferences
Predmet:
Human-centered computing
Human-centered computing > Collaborative and social computing
Human-centered computing > Collaborative and social computing > Collaborative and social computing theory, concepts and paradigms
Human-centered computing > Collaborative and social computing > Collaborative and social computing theory, concepts and paradigms > Social networks
Human-centered computing > Visualization
Human-centered computing > Visualization > Visualization application domains
Human-centered computing > Visualization > Visualization application domains > Geographic visualization
Information systems
Information systems > Information retrieval
Information systems > Information systems applications
Information systems > Information systems applications > Data mining
Information systems > Information systems applications > Spatial-temporal systems
Information systems > Information systems applications > Spatial-temporal systems > Geographic information systems
Information systems > Information systems applications > Spatial-temporal systems > Location based services
Location Based Social Networks
Diversified Local Users
Foursquare
ISBN:1450349935, 9781450349932
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Popis
Shrnutí:Identifying a set of diversified users who are local residents in a city is an important task for a wide spectrum of applications such as target ads of local business, surveys and interviews, and personalized recommendations. While many previous studies have investigated the problem of identifying the local users in a given area using online social network information (e.g., geotagged posts), few methods have been developed to solve the diversified user identification problem. In this paper, we propose a new analytical framework, Diversified Local Users Finder (DLUF), to accurately identify a set of diversified local users using a principled approach. In particular, the DLUF scheme first defines a new distance metric that measures the diversity between local users from physical dimension. The DLUF scheme then provides a solution to find the set of local users with maximum diversity. The performance of DLUF scheme is compared to several representative baselines using two real world datasets obtained from Foursquare application. We observe that the DLUF scheme accurately identifies the local users with a great diversity and significantly outperforms the compared baselines.
ISBN:1450349935
9781450349932
DOI:10.1145/3110025.3110159