Identifying Users' Concerns in Lodging Sharing Economy Using Unsupervised Machine Learning Approach

被引:0
|
作者
Al-Ramahi, Mohammad [1 ]
Ahmed, Ali [2 ]
机构
[1] Texas A&M Univ San Antonio, Dept Comp & Cyber Secur, San Antonio, TX 78224 USA
[2] Univ Massachusetts Lowell, Manning Sch Business, Lowell, MA USA
关键词
Unsupervised machine learning; Topic Mining; Sharing economy; Online user reviews;
D O I
10.1109/ICDIS.2019.00031
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Lodging sharing economy services have exponentially grown in the last decade. The users of this new economic model are also facing many challenges and difficulties which are less commonly known to the research community. In this research, we have used unique data collection and an unsupervised machine learning method to uncover the needs and concerns of the users of this new economic model. We focused on the lodging company, Airbnb, to use as our test case. Similar approaches can also be applied on other sharing economies companies. The results reported current lodging sharing services lacks regulations for disputes. Findings also revealed safety concerns of the users. Overall, this research contributes with practical managerial implications and guidelines for future research while implementing a new data collection methodology.
引用
收藏
页码:160 / 166
页数:7
相关论文
共 50 条
  • [1] IDENTIFYING CLINICAL SUBTYPES OF LONG COVID: AN UNSUPERVISED MACHINE LEARNING APPROACH
    Munsell, M.
    Friedman, M.
    Menzin, J.
    [J]. VALUE IN HEALTH, 2023, 26 (06) : S284 - S284
  • [2] Identifying strong lenses with unsupervised machine learning using convolutional autoencoder
    Cheng, Ting-Yun
    Li, Nan
    Conselice, Christopher J.
    Aragon-Salamanca, Alfonso
    Dye, Simon
    Metcalf, Robert B.
    [J]. MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY, 2020, 494 (03) : 3750 - 3765
  • [3] Identifying Kinematic Structures in Simulated Galaxies Using Unsupervised Machine Learning
    Du, Min
    Ho, Luis C.
    Zhao, Dongyao
    Shi, Jingjing
    Debattista, Victor P.
    Hernquist, Lars
    Nelson, Dylan
    [J]. ASTROPHYSICAL JOURNAL, 2019, 884 (02):
  • [4] Classification of Users of a Health Service Provider Using Unsupervised Machine Learning Methods
    Marlon David Arango-Abella
    Juan Carlos Figueroa-García
    [J]. SN Computer Science, 5 (5)
  • [5] Identifying diseases symptoms and general rules using supervised and unsupervised machine learning
    Sogandi, Fatemeh
    [J]. SCIENTIFIC REPORTS, 2024, 14 (01):
  • [6] Highway Project Clustering Using Unsupervised Machine Learning Approach
    Alikhani, Hamed
    Jeong, H. David
    [J]. COMPUTING IN CIVIL ENGINEERING 2021, 2022, : 172 - 179
  • [7] Identifying Patterns of Breast Cancer Genetic Signatures using Unsupervised Machine Learning
    Hamoudi, Rifat
    Bettayeb, Meriem
    Alsaafin, Areej
    Hachim, Mahmood
    Nassir, Qassim
    Nassif, Ali Bou
    [J]. 2019 IEEE INTERNATIONAL CONFERENCE ON IMAGING SYSTEMS & TECHNIQUES (IST 2019), 2019,
  • [8] An Approach for Clustering of Seismic Events using Unsupervised Machine Learning
    Karmenova, Markhaba
    Tlebaldinova, Aizhan
    Krak, Iurii
    Denissova, Natalya
    Popova, Galina
    Zhantassova, Zheniskul
    Ponkina, Elena
    Gyorok, Gyorgy
    [J]. ACTA POLYTECHNICA HUNGARICA, 2022, 19 (05) : 7 - 22
  • [9] Identifying Outliers in Astronomical Images with Unsupervised Machine Learning
    Yang Han
    Zhiqiang Zou
    Nan Li
    Yanli Chen
    [J]. Research in Astronomy and Astrophysics, 2022, 22 (08) : 76 - 86
  • [10] Identifying topological order through unsupervised machine learning
    Joaquin F. Rodriguez-Nieva
    Mathias S. Scheurer
    [J]. Nature Physics, 2019, 15 : 790 - 795