Article

A user similarity calculation based on the location for social network services

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DASFAA'11: Proceedings of the 16th international conference on Database systems for advanced applications - Volume Part I

Pages 38 - 52

Published: 22 April 2011 Publication History

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Abstract

The online social network services have been growing rapidly over the past few years, and the social network services can easily obtain the locations of users with the recent increasing popularity of the GPS enabled mobile device. In the social network, calculating the similarity between users is an important issue. The user similarity has significant impacts to users, communities and service providers by helping them acquire suitable information effectively.

There are numerous factors such as the location, the interest and the gender to calculate the user similarity. The location becomes a very important factor among them, since nowadays the social network services are highly coupled with the mobile device which the user holds all the time. There have been several researches on calculating the user similarity. However, most of them did not consider the location. Even if some methods consider the location, they only consider the physical location of the user which cannot be used for capturing the user's intention.

We propose an effective method to calculate the user similarity using the semantics of the location. By using the semantics of the location, we can capture the user's intention and interest. Moreover, we can calculate the similarity between different locations using the hierarchical location category. To the best of our knowledge, this is the first research that uses the semantics of the location in order to calculate the user similarity. We evaluate the proposed method with a real-world use case: finding the most similar user of a user. We collected more than 251,000 visited locations over 591 users from foursquare. The experimental results show that the proposed method outperforms a popular existing method calculating the user similarity.

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Cited By

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Zhao WFan FWen JChang E(2018)Joint Representation Learning for Location-Based Social Networks with Multi-Grained Sequential ContextsACM Transactions on Knowledge Discovery from Data10.1145/312787512:2(1-21)Online publication date: 23-Jan-2018
https://dl.acm.org/doi/10.1145/3127875
Albahri AZaidan AAlbahri OZaidan BAlsalem M(2018)Real-Time Fault-Tolerant mHealth SystemJournal of Medical Systems10.1007/s10916-018-0983-942:8(1-56)Online publication date: 1-Aug-2018
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Mazumdar PPatra BBabu KLock R(2018)Hidden location prediction using check-in patterns in location-based social networksKnowledge and Information Systems10.1007/s10115-018-1170-557:3(571-601)Online publication date: 1-Dec-2018
https://dl.acm.org/doi/10.1007/s10115-018-1170-5
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Published In

DASFAA'11: Proceedings of the 16th international conference on Database systems for advanced applications - Volume Part I

April 2011

579 pages

ISBN:9783642201486

Editors:
Jeffrey Xu Yu
The Chinese University of Hong Kong, Department of Systems Engineering and Engineering Management, Hong Kong, China
,
Myoung Ho Kim
Korea Advanced Institute of Science and Technology (KAIST), Department of Computer Science, Daejeon, Korea
,
Rainer Unland
University of Duisburg-Essen, Institute for Computer Science and Business Information Systems, Essen, Germany

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 22 April 2011

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Zhao WFan FWen JChang E(2018)Joint Representation Learning for Location-Based Social Networks with Multi-Grained Sequential ContextsACM Transactions on Knowledge Discovery from Data10.1145/312787512:2(1-21)Online publication date: 23-Jan-2018
https://dl.acm.org/doi/10.1145/3127875
Albahri AZaidan AAlbahri OZaidan BAlsalem M(2018)Real-Time Fault-Tolerant mHealth SystemJournal of Medical Systems10.1007/s10916-018-0983-942:8(1-56)Online publication date: 1-Aug-2018
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Mazumdar PPatra BBabu KLock R(2018)Hidden location prediction using check-in patterns in location-based social networksKnowledge and Information Systems10.1007/s10115-018-1170-557:3(571-601)Online publication date: 1-Dec-2018
https://dl.acm.org/doi/10.1007/s10115-018-1170-5
Jonathan AChandra AWeissman JAnjum ASill AFox GChen Y(2017)Locality-Aware Load Sharing in Mobile Cloud ComputingProceedings of the10th International Conference on Utility and Cloud Computing10.1145/3147213.3147228(141-150)Online publication date: 5-Dec-2017
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Kanza YKravi ESafra ESagiv Y(2017)Location-Based Distance Measures for Geosocial SimilarityACM Transactions on the Web10.1145/305495111:3(1-32)Online publication date: 3-Jul-2017
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Yang CSun MZhao WLiu ZChang E(2017)A Neural Network Approach to Jointly Modeling Social Networks and Mobile TrajectoriesACM Transactions on Information Systems10.1145/304165835:4(1-28)Online publication date: 16-Aug-2017
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Zhao WZhou NZhang WWen JWang SChang E(2016)A Probabilistic Lifestyle-Based Trajectory Model for Social Strength Inference from Human Trajectory DataACM Transactions on Information Systems10.1145/294806435:1(1-28)Online publication date: 3-Sep-2016
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Lv MChen LChen G(2013)Mining user similarity based on routine activitiesInformation Sciences: an International Journal10.1016/j.ins.2013.02.050236(17-32)Online publication date: 1-Jul-2013
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Liu WSun WChen CHuang YJing YChen K(2012)Circle of friend query in geo-social networksProceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part II10.1007/978-3-642-29035-0_9(126-137)Online publication date: 15-Apr-2012
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Mining user similarity based on location history

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