Data Harvesting, Curation and Fusion Model to Support Public Service Recommendations for e-Governments
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Sedrakyan, Gayane; De Vocht, Laurens; Alonso, Juncal; Escalante, Marisa; Orue-Echevarria, Leire; [et al.]Date
2018-01Keywords
Architectural Model
Recommendation Generation
Public Administration
Public Services
Data
Data Harvesting
Data Curation
Data Fusion
Linked Data
E-Government
Abstract
This work reports on early results from CITADEL project that aims at creating an ecosystem of best practices, tools, and recommendations to transform Public Administrations with more efficient, inclusive and citizen-centric services. The goal of the recommendations is to support Governments to find out why citizens stop using public services, and use this information to re-adjust provision to bring these citizens back in. Furthermore, it will help identifying why citizens are not using a given public service (due to affordability, accessibility, lack of knowledge, embarrassment, lack of interest, etc.) and, where appropriate, use this information to make public services more attractive, so they start using the services. While recommender systems can enhance experiences by providing targeted information, the entry barriers in terms of data acquisition are very high, often limiting recommender solutions to closed systems of user/context models. The main focus of this work is to provide an ...
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conference output