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Towards Diversified Local Users Identification Using Location Based Social Networks

Authors:

Shenglong ZhuAuthors Info & Claims

ASONAM '17: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017

Pages 115 - 118

https://doi.org/10.1145/3110025.3110159

Published: 31 July 2017 Publication History

Abstract

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.

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Cited By

Huang CWang DTao JMann B(2020)On Physical-Social-Aware Localness Inference by Exploring Big Data from Location-Based ServicesIEEE Transactions on Big Data10.1109/TBDATA.2017.27265516:4(679-690)Online publication date: 1-Dec-2020
https://doi.org/10.1109/TBDATA.2017.2726551
Yu KXu PSong SFeng HQiu SLiu H(2019)Location Traceability of Social Media Analysis in Urban ComplexProceedings of the 2019 2nd International Conference on Data Science and Information Technology10.1145/3352411.3352444(210-218)Online publication date: 19-Jul-2019
https://dl.acm.org/doi/10.1145/3352411.3352444
Cai CLi LZeng DMa H(2019)Exploring Writing Pattern with Pop Culture Ingredients for Social User Modeling2019 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN.2019.8852187(1-8)Online publication date: Jul-2019
https://doi.org/10.1109/IJCNN.2019.8852187

Towards Diversified Local Users Identification Using Location Based Social Networks
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction paradigms
2. Information systems
  1. Information systems applications

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cover image ACM Conferences

ASONAM '17: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017

July 2017

698 pages

ISBN:9781450349932

DOI:10.1145/3110025

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 31 July 2017

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ASONAM '17: Advances in Social Networks Analysis and Mining 2017

July 31 - August 3, 2017

Sydney, Australia

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Overall Acceptance Rate 116 of 549 submissions, 21%

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Cited By

Huang CWang DTao JMann B(2020)On Physical-Social-Aware Localness Inference by Exploring Big Data from Location-Based ServicesIEEE Transactions on Big Data10.1109/TBDATA.2017.27265516:4(679-690)Online publication date: 1-Dec-2020
https://doi.org/10.1109/TBDATA.2017.2726551
Yu KXu PSong SFeng HQiu SLiu H(2019)Location Traceability of Social Media Analysis in Urban ComplexProceedings of the 2019 2nd International Conference on Data Science and Information Technology10.1145/3352411.3352444(210-218)Online publication date: 19-Jul-2019
https://dl.acm.org/doi/10.1145/3352411.3352444
Cai CLi LZeng DMa H(2019)Exploring Writing Pattern with Pop Culture Ingredients for Social User Modeling2019 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN.2019.8852187(1-8)Online publication date: Jul-2019
https://doi.org/10.1109/IJCNN.2019.8852187

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