Explainable multi-task convolutional neural network framework for electronic petition tag recommendation-Reference-Cited by-同舟云学术

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Explainable multi-task convolutional neural network framework for electronic petition tag recommendation

Published:2023-05 Issue: Volume:59 Page:101263
ISSN:1567-4223
Container-title:Electronic Commerce Research and Applications
language:en
Short-container-title:Electronic Commerce Research and Applications

Author:

Yang Zekun^ORCID,Feng Juan

Funder

National Natural Science Foundation of China

Renmin University of China

Fundamental Research Funds for the Central Universities

Publisher

Elsevier BV

Subject

Management of Technology and Innovation,Marketing,Computer Networks and Communications,Computer Science Applications

Reference107 articles.

1. E-government research insights: Text mining analysis;Abu-Shanab;Electron. Commer. Res. Appl.,2019

2. Citizen participation and effectiveness of e-petition: Sutharyakeralam-India;Alathur;Transf. Govern.: People Process Policy,2012

3. E-participation model for Kuwait e-government;Aljazzaf;(IJACSA) Int. J. Adv. Comput. Sci. Appl.,2020

4. Customer empowerment: Does it influence electronic government success? A citizen-centric perspective;Alshibly;Electron. Commer. Res. Appl.,2015

5. Online political participation in Spain: The impact of traditional and internet resources;Anduiza;J. Inf. Technol. Politics,2010

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2. A BERT-based Approach to Alleviate Civic Tech Tools Overcrowding: A case study of Taiwan's JOIN e-petition system;Proceedings of the 25th Annual International Conference on Digital Government Research;2024-06-11

3. A Multi-Label Classifier for Online Petition Systems;Proceedings of the 25th Annual International Conference on Digital Government Research;2024-06-11

4. The Use of Artificial Intelligence in eParticipation: Mapping Current Research;Future Internet;2024-06-03

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