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

doi:10.1016/j.elerap.2023.101263

2023-05-01

PRE

杨泽堃 (Zekun Yang)

冯娟 (Juan Feng) *

DOI：10.1016/j.elerap.2023.101263

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摘要

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Electronic petition (e-petition) is an electronic government (e-government) service that allows citizens to file petitions to governments via the internet. The complexity of the e-petition filing process and the unexplainable e-government tools would reduce the perceived ease of use and trust of users, which causes citizens to make errors when tagging their e-petition. These errors may lead to failure and delay in resolving the e-petition, which further restrains citizen engagement and electronic participation (e-participation) adoption on e-petition platforms (EPP). The purpose of this study is to develop an explainable tag recommender system to assist citizens in tagging their e-petitions. Specifically, we design an explainable multi-task learning framework for e-petition tag recommendation based on convolutional neural networks and layer-wise relevance propagation. We also conduct both quantitative and qualitative experiments to demonstrate the recommendation effec-tiveness as well as interpretability of our model. This is among the first attempt to design an e-petition tag recommender system and an explainable e-government recommender system. The practical implications of our research are two-fold. For citizens, our proposed model recommends more accurate tags with human -understandable explanations, which could assist citizens' tagging decisions and increase the possibility for an e-petition to be resolved. For governments, the e-petition service quality of governments would be enhanced, which further promotes e-participation adoption, citizen engagement, and e-government success on EPPs.

Keyword:

Recommender systems

E-government

E-petition

Explainable machine learning

Multi-task learning

User-generated content