Geographical and Temporal Similarity Measurement on Location-based Social Networks
2013 (English)In: Proceedings of the Second ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems / [ed] Chi-Yin Chow and Shashi Shekhar, New York, NY, USA: Association for Computing Machinery (ACM), 2013, 30-34 p.Conference paper (Refereed)
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.
Place, publisher, year, edition, pages
New York, NY, USA: Association for Computing Machinery (ACM), 2013. 30-34 p.
Cluster, location-based social networks, temporal scale, user similarity
Research subject Geodesy and Geoinformatics; Computer Science
IdentifiersURN: urn:nbn:se:kth:diva-164166DOI: 10.1145/2534190.2534192ISBN: 978-1-4503-2531-8OAI: oai:DiVA.org:kth-164166DiVA: diva2:804946
MobiGIS'13 Second ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems
QC 2015050212015-04-142015-04-142015-05-21Bibliographically approved