政务服务智能化演进：用户行为数据的视角

doi:10.3772/j.issn.1000-0135.2025.11.008

情报学报

2025, Vol. 44

Issue (11): 1458-1469 DOI: 10.3772/j.issn.1000-0135.2025.11.008

情报理论与方法

本期目录 | 过刊浏览 | 高级检索

政务服务智能化演进：用户行为数据的视角

胡广伟^1,2, 贺荒兰^1,2

1.南京大学信息管理学院，南京 210023
2.南京大学政务数据资源研究所，南京 210023

Evolution of Intelligent Governance Service: From the Perspective of User Behavior Data

Hu Guangwei^1,2, He Huanglan^1,2

1.School of Information Management, Nanjing University, Nanjing 210023
2.Government Data Resources Institution of Nanjing University, Nanjing 210023

摘要
图/表
参考文献
相关文章 (15)

全文: PDF (3348 KB) HTML (1 KB)
输出: BibTeX | EndNote (RIS)

摘要在电子政务服务的演进过程中，智能化成为推动服务质量提升的关键因素之一。电子政务服务的智能化不仅体现在技术层面，更关乎对用户需求的深刻理解和即时响应。但目前电子政务服务系统普遍缺少对用户深层需求的洞察，部分原因可能在于缺失了对用户行为数据的采集与分析，难以前瞻性预测用户的个性化、动态性需求。为此，本文构建了电子政务服务内外部用户行为的分析框架，支撑对内部用户行为和外部用户行为进行系统整合与全面分析；在此基础上，解析用户行为数据支撑政务服务智能化的原理，并选取智能信息推送服务作为应用实例，揭示用户行为数据对政务服务智能演化的基础性和重要性作用。特别强调，在开发和应用用户行为数据时，应明确目标范围、解决黑箱及算法偏见问题、关注最大化公共价值、平衡数据开放与隐私保护、担当好“算法治理”角色、加强数据单元的互联互通、重视政策分析及相关数据应用，以期为政务服务的智能进化提供路径参考。

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	胡广伟
	贺荒兰

关键词 ：电子政务, 政务服务, 用户行为, 大语言模型, 智能进化

收稿日期: 2025-03-02

基金资助:国家社会科学基金重大项目“大数据驱动的城乡社区服务体系精准化构建研究”（20&ZD154）。

作者简介: 胡广伟，1975年生，博士，教授，博士生导师，研究方向为政务大数据、数据智能与数据治理、价值共创等，E-mail：hugw@ nju.edu.cn；贺荒兰，2001年生，硕士研究生，研究方向为网络问政、电子政务、政务大数据；

引用本文:

胡广伟, 贺荒兰. 政务服务智能化演进：用户行为数据的视角[J]. 情报学报, 2025, 44(11): 1458-1469.
Hu Guangwei, He Huanglan. Evolution of Intelligent Governance Service: From the Perspective of User Behavior Data. 情报学报, 2025, 44(11): 1458-1469.

链接本文:

https://qbxb.istic.ac.cn/CN/10.3772/j.issn.1000-0135.2025.11.008 或 https://qbxb.istic.ac.cn/CN/Y2025/V44/I11/1458

1 Deshpande D, Deshpande S. Online user behavior: a decade’s perspective[C]// Proceedings of the 2017 International Conference on Trends in Electronics and Informatics. Piscataway: IEEE, 2017: 977-984.
2 Martín A G, Fernández-Isabel A, de Diego I M, et al. A survey for user behavior analysis based on machine learning techniques: current models and applications[J]. Applied Intelligence, 2021, 51(8): 6029-6055.
3 张明, 肖鹏, 李玉娟. 通过用户行为分析改进微信公众号用户服务体验的方法探析[J]. 信息系统工程, 2019(5): 19-21.
4 王晓春, 李生, 杨沐昀, 等. 查询会话中的用户行为分析[J]. 哈尔滨工业大学学报, 2011, 43(5): 76-78, 105.
5 Rodriguez Müller A P, Lerusse A, Steen T, et al. Understanding channel choice in users’ reporting behavior: Evidence from a smart mobility case[J]. Government Information Quarterly, 2021, 38(1): 101540.
6 Eom S J, Hwang H, Kim J H. Can social media increase government responsiveness? A case study of Seoul, Korea[J]. Government Information Quarterly, 2018, 35(1): 109-122.
7 Shwartz-Asher D, Chun S A, Adam N R. Social media user behavior analysis in e-government context[C]// Proceedings of the 17th International Digital Government Research Conference on Digital Government Research. New York: ACM Press, 2016: 39-48.
8 Al-Mushayt O S. Automating e-government services with artificial intelligence[J]. IEEE Access, 2019, 7: 146821-146829.
9 何娟. 基于用户个人及群体画像相结合的图书个性化推荐应用研究[J]. 情报理论与实践, 2019, 42(1): 129-133, 160.
10 谢康, 吴记, 肖静华. 基于大数据平台的用户画像与用户行为分析[J]. 中国信息化, 2018(3): 100-104.
11 曾子明, 金鹏. 智慧图书馆个性化推荐服务体系及模式研究[J]. 图书馆杂志, 2015, 34(12): 16-22.
12 耿立校, 晋高杰, 李亚函, 等. 基于改进内容过滤算法的高校图书馆文献资源个性化推荐研究[J]. 图书情报工作, 2018, 62(21): 112-117.
13 Kong L Y, Ding H, Hu G W. GCNSLIM: graph convolutional network with sparse linear methods for e-government service recommendation[J]. Knowledge-Based Systems, 2024, 292: 111593.
14 Dreyer S, Olivotti D, Lebek B, et al. Focusing the customer through smart services: a literature review[J]. Electronic Markets, 2019, 29: 55-78.
15 Lee J Y, Kim M K, La H J, et al. A software framework for enabling smart services[C]// Proceedings of the Fifth IEEE International Conference on Service-Oriented Computing and Applications. Piscataway: IEEE, 2012: 1-8.
16 Hu G W, Yan J Q, Pan W W, et al. The influence of public engaging intention on value co-creation of e-government services[J]. IEEE Access, 2019, 7: 111145-111159.
17 Firdaus V A H, Saputra P Y, Suprianto D. Intelligence chatbot for Indonesian law on electronic information and transaction[J]. IOP Conference Series: Materials Science and Engineering, 2020, 830(2): 022089.
18 Zheng Y Q, Yu H, Cui L Z, et al. SmartHS: an AI platform for improving government service provision[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2018, 32(1): 7704-7711.
19 Kuziemski M, Misuraca G. AI governance in the public sector: three tales from the frontiers of automated decision-making in democratic settings[J]. Telecommunications Policy, 2020, 44(6): 101976.
20 Adnan Khan M, Kanwal A, Abbas S, et al. Intelligent model for predicting the quality of services violation[J]. Computers, Materials & Continua, 2022, 71(2): 3607-3619.
21 Sipsas K, Alexopoulos K, Xanthakis V, et al. Collaborative maintenance in flow-line manufacturing environments: an industry 4.0 approach[J]. Procedia CIRP, 2016, 55: 236-241.
22 Da’u A, Salim N. Recommendation system based on deep learning methods: a systematic review and new directions[J]. Artificial Intelligence Review, 2020, 53(4): 2709-2748.
23 黄梅银, 易兰丽, 王理达. 政务服务中的智能推送: 需求、应用模式和实现路径[J]. 电子政务, 2020(2): 11-20.
24 单伟力, 张晗, 李丹. 智能画像技术和服务推荐技术在电子税务局中的应用场景探讨[J]. 税务研究, 2022(4): 62-68.
25 Zuiderwijk A, Chen Y C, Salem F. Implications of the use of artificial intelligence in public governance: a systematic literature review and a research agenda[J]. Government Information Quarterly, 2021, 38(3): 101577.
26 Al-Besher A, Kumar K. Use of artificial intelligence to enhance e-government services[J]. Measurement: Sensors, 2022, 24: 100484.
27 Engin Z, Treleaven P. Algorithmic government: automating public services and supporting civil servants in using data science technologies[J]. The Computer Journal, 2019, 62(3): 448-460.
28 Fatima S, Desouza K C, Dawson G S. National strategic artificial intelligence plans: a multi-dimensional analysis[J]. Economic Analysis and Policy, 2020, 67: 178-194.
29 Pawlowski C, Scholta H. A taxonomy for proactive public services[J]. Government Information Quarterly, 2023, 40(1): 101780.
30 Martin N, Gregor S, Hart D. Using a common architecture in Australian e-government: the case of smart service Queensland[C]// Proceedings of the 6th International Conference on Electronic Commerce. New York: ACM Press, 2004: 516-525.
31 Wirtz B W, Weyerer J C, Sturm B J. The dark sides of artificial intelligence: An integrated AI governance framework for public administration[J]. International Journal of Public Administration, 2020, 43(9): 818-829.
32 刘玮, 王锋. 政务服务智能化创新的演化、风险与图景——基于场域视角的分析[J]. 电子政务, 2024(2): 79-88.
33 Wirtz B W, Müller W M. An integrated artificial intelligence framework for public management[J]. Public Management Review, 2019, 21(7): 1076-1100.
34 Sulistiyono S, Irawan A, Rizal S. Sistem informasi penerimaan peserta didik baru berbasis web pada balai latihan kerja (BLK) Kota Cilegon[J]. Jurnal Sistem Informasi dan Informatika, 2020, 3(1): 27-39.
35 Berdykhanova D, Dehghantanha A, Hariraj K. Trust challenges and issues of e-government: e-tax prospective[C]// Proceedings of 2010 International Symposium on Information Technology. Piscataway: IEEE, 2010: 1015-1019.
36 Chen T, Guo W S, Gao X, et al. AI-based self-service technology in public service delivery: user experience and influencing factors[J]. Government Information Quarterly, 2021, 38(4): 101520.
37 陈媛媛, 陈志鹏. 公众参与情境下的政府数字治理创新路径——以北京网络问政平台主题分析为例[J]. 图书馆论坛, 2024, 44(11): 78-89.
38 陆敬筠, 仲伟俊, 朱晓峰. 电子政务服务公众参与模型及实证研究[J]. 情报科学, 2010, 28(8): 1247-1252.
39 Voorberg W H, Bekkers V J J M, Tummers L G. A systematic review of co-creation and co-production: embarking on the social innovation journey[J]. Public Management Review, 2015, 17(9): 1333-1357.
40 胡广伟. 数据思维[M]. 北京: 清华大学出版社, 2020.
41 Vargo S L, Lusch R F. Evolving to a new dominant logic for marketing[J]. Journal of Marketing, 2004, 68(1): 1-17.
42 罗伯特B.丹哈特, 珍妮特V.丹哈特. 新公共服务: 服务而非掌舵[J]. 刘俊生,译. 张庆东, 校. 中国行政管理, 2002(10): 38-44.
43 司文峰, 胡广伟. “互联网+政务服务”价值共创概念、逻辑、路径与作用[J]. 电子政务, 2018(3): 75-80.
44 刘柳, 胡广伟. 电子政务服务价值共创及战略要素分析[J]. 电子政务, 2015(4): 90-97.
45 胡广伟, 范兆媛. 数据生产: 概念、场景、技术与审思[J]. 信息资源管理学报, 2024, 14(5): 14-21.
46 Lee G, Kwak Y H. An open government maturity model for social media-based public engagement[J]. Government Information Quarterly, 2012, 29(4): 492-503.
47 Reddick C, Ganapati S. Open government achievement and satisfaction in US federal agencies: survey evidence for the three pillars[J]. Journal of E-Governance, 2011, 34(4): 193-202.
48 Lee-Geiller S, Lee T D. Using government websites to enhance democratic e-governance: a conceptual model for evaluation[J]. Government Information Quarterly, 2019, 36(2): 208-225.
49 李散散. 基于用户行为分析和LDA模型的数字媒体推荐系统的设计与实现[J]. 现代电子技术, 2020, 43(7): 146-149, 154.
50 周云霞, 栗磊. 基于数据库用户行为分析的改进FP-Growth算法[J]. 科学技术与工程, 2011, 11(18): 4380-4383.
51 王健, 毋丽丽, 裴春琴, 等. 基于改进k-均值聚类算法的汽车用户行为分析方法研究[J]. 燕山大学学报, 2023, 47(3): 229-235, 245.
52 Zhao W X, Zhou K, Li J Y, et al. A survey of large language models[OL]. (2025-03-11). https://arxiv.org/pdf/2303.18223.
53 Liang H B, Zhang X H, Hong X T, et al. Reinforcement learning enabled dynamic resource allocation in the internet of vehicles[J]. IEEE Transactions on Industrial Informatics, 2021, 17(7): 4957-4967.
54 吴春梅. 现代智能优化算法的研究综述[J]. 科技信息, 2012(8): 31, 33.