Local experts finding using user comments in location-based social networks

被引:7
|
作者
Cao, Jiuxin [1 ]
Yang, Yuntao [2 ]
Cao, Biwei [3 ]
Xue, Lingyun [2 ]
Li, Shancang [4 ]
Iqbal, Muddesar [5 ]
Mumtaz, Shahid [6 ]
机构
[1] Southeast Univ, Sch Cyber Sci & Engn, Key Lab Comp Network Technol Jiangsu Prov, Nanjing, Jiangsu, Peoples R China
[2] Southeast Univ, Sch Comp Sci & Engn, Nanjing, Jiangsu, Peoples R China
[3] Australia Natl Univ, Coll Engn & Comp Sci, Canberra, ACT, Australia
[4] Univ West England, Dept Comp Sci & Creat Technol, Bristol, Avon, England
[5] London South Bank Univ, Sch Comp, London, England
[6] Univ Santiago, Inst Telecomunicacoes, Aveiro, Portugal
基金
中国国家自然科学基金;
关键词
ARCHITECTURE;
D O I
10.1002/ett.3600
中图分类号
TN [电子技术、通信技术];
学科分类号
0809 ;
摘要
The opinions of local experts in the location-based social network are of great significance to the collection and dissemination of local information. In this paper, we investigated in-depth how the user comments can be used to identify the local expert over social networks. We first illustrate the existences of potential local experts in a social network using a scored model by considering the personal profiles, comments, friend relationship, and location preferences. Then, a multidimensional model is proposed to evaluate the local expert candidates and a local expert discovery algorithm is proposed to identify local experts. Meanwhile, a scoring algorithm is proposed to train the weights in the model. Finally, an expert recommendation list can be given based on the score ranks of the candidates. Experimental results demonstrate the effectiveness of proposed model and algorithms.
引用
收藏
页数:18
相关论文
共 50 条
  • [1] Modeling User Mobility for Location Promotion in Location-based Social Networks
    Zhu, Wen-Yuan
    Peng, Wen-Chih
    Chen, Ling-Jyh
    Zheng, Kai
    Zhou, Xiaofang
    [J]. KDD'15: PROCEEDINGS OF THE 21ST ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, 2015, : 1573 - 1582
  • [2] Finding Geo-Social Cohorts in Location-Based Social Networks
    Saleem, Muhammad Aamir
    Calders, Toon
    Pedersen, Torben Bach
    Karras, Panagiotis
    [J]. WEB AND BIG DATA, APWEB-WAIM 2021, PT II, 2021, 12859 : 368 - 383
  • [3] Mining User Behavior and Similarity in Location-based Social Networks
    Zou, Zhiqiang
    Xie, Xingyu
    Sha, Chao
    [J]. 2015 SEVENTH INTERNATIONAL SYMPOSIUM ON PARALLEL ARCHITECTURES, ALGORITHMS AND PROGRAMMING (PAAP), 2015, : 167 - 171
  • [4] A contextualized and personalized model to predict user interest using location-based social networks
    Li, Ming
    Sagl, Guenther
    Mburu, Lucy
    Fan, Hongchao
    [J]. COMPUTERS ENVIRONMENT AND URBAN SYSTEMS, 2016, 58 : 97 - 106
  • [5] An empirical approach for fake user detection in location-based social networks
    Melia-Segui, Joan
    Bart, Eugene
    Zhang, Rui
    Brdiczka, Oliver
    [J]. JOURNAL OF AMBIENT INTELLIGENCE AND SMART ENVIRONMENTS, 2017, 9 (06) : 643 - 657
  • [6] Learning evolving user's behaviors on location-based social networks
    Wu, Ruizhi
    Luo, Guangchun
    Jin, Qi
    Shao, Junming
    Lu, Chang-Tien
    [J]. GEOINFORMATICA, 2020, 24 (03) : 713 - 743
  • [7] Learning evolving user’s behaviors on location-based social networks
    Ruizhi Wu
    Guangchun Luo
    Qi Jin
    Junming Shao
    Chang-Tien Lu
    [J]. GeoInformatica, 2020, 24 : 713 - 743
  • [8] Analyzing Online Location-Based Social Networks for Malicious User Detection
    Hussain, Ahsan
    Keshavamurthy, Bettahally N.
    [J]. RECENT FINDINGS IN INTELLIGENT COMPUTING TECHNIQUES, VOL 1, 2019, 707 : 463 - 471
  • [9] Privacy in location-based social networks: privacy scripts & user practices
    Coppens, Paulien
    Veeckman, Carina
    Claeys, Laurence
    [J]. JOURNAL OF LOCATION BASED SERVICES, 2015, 9 (01) : 1 - 15
  • [10] Spatio-semantic user profiles in location-based social networks
    Mohamed S.
    Abdelmoty A.I.
    [J]. International Journal of Data Science and Analytics, 2017, 4 (2) : 127 - 142