NDLI: Geographical and temporal similarity measurement in location-based social networks

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Content Provider	ACM Digital Library
Author	Jiang, Yanli Yuan, Zhengwu Gidófalvi, Győző
Abstract	Using "check-in" data gathered from location-based social networks, this paper proposes to measure the similarity of users by considering the geographical and the temporal aspect of their geographical and temporal aspects of their "check-ins". Temporal neighborhood is added to support the time dimension on the basis of the traditional DBSCAN clustering algorithm, which determines the similarity among users at different scales using the classical Vector Space Model (VSM) with vectors composed of the amount of visits in different cluster area. The spatio-temporal similarity of the user behaviors are obtained through overlapping the different weighted user similarity values. The experimental results show that the proposed approach is effective in measuring user similarity in location-based social networks.
Starting Page	30
Ending Page	34
Page Count	5
File Format	PDF
ISBN	9781450325318
DOI	10.1145/2534190.2534192
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-11-05
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Temporal scale Cluster Location-based social networks User similarity
Content Type	Text
Resource Type	Article

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Geographical and temporal similarity measurement in location-based social networks