Designing Recommender Systems for the Common Good

Research output: Chapter in Book/Report/Conference proceedingConference paper

3 Citations (Scopus)

Abstract

The growing inclusion of information and communication tech- nologies in our everyday life sets the scene for the development of personalized public services. Their public character brings along challenges that have not necessarily been dealt with in commercial applications, especially in terms of optimizing for the common good which requires moving away from a purely personalized-oriented approach. In this paper, we claim that to address these challenges, we can learn from two best practices in the design of digital public services: participatory design and open data.
Original languageEnglish
Title of host publicationAdjunct Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization
PublisherACM
Pages276-278
Number of pages3
ISBN (Electronic)978-1-4503-7950-2/20/07
DOIs
Publication statusPublished - 14 Jul 2020
Event28th ACM Conference on User Modeling, Adaptation and Personalization - , Italy
Duration: 14 Jul 202017 Jul 2020

Conference

Conference28th ACM Conference on User Modeling, Adaptation and Personalization
Abbreviated titleUMAP20
CountryItaly
Period14/07/2017/07/20

Keywords

  • multi-stakeholder recommendation
  • open data
  • participatory design
  • personalization

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