Recommender Systems for Citizens

With the growing amount of people living in ever denser areas, there is an increasing demand for novel Information and Communication Technology (ICT) to support the complex social and environmental interactions of citizens, and to improve their quality of life. A typical example is the concept and construct of the ”smart city”, which has been introduced to highlight the importance of ICT for enhancing the competitive profile of a city.

This workshop focuses on citizens’ recommender systems. This particular type of recommender systems, while still belonging to the broad area of recommendation, differs from conventional recommender systems both in terms of ownership and purpose. Unlike conventional recommender systems driven by a per-click business model, citizens’ recommender systems are run by citizen themselves and serve the society as a whole. By soliciting behavioural data from citizens, the systems can make recommendations to optimally improve the living experiences of citizens in a society.

Such behavioural data used to be scarce, hindering the development of citizens’ recommender systems. The emergence of social data, i.e. data generated by people during their activities in a social environment, available through new sources (e.g. social media, mobile phones, sensor networks), brings great opportunities for studying the usefulness of aggregated citizen behaviours. Social data contain important signals on citizen-environment and citizen-citizen interactions. By exploiting such data, recommender systems have the potential to play an important role in improving citizen satisfaction in multiple societal contexts, and to mitigate the information overload problem in societal decision making processes.

At the same time, while comprehensively describing people’s lives, social data are characterised by an intrinsic diversity, manifested through multiple dimensions. These include the targeted citizen population (e.g., residents, commuters), types of activities (e.g., transportation, working, entertainment), and the context (e.g., when and where). Despite the large body of literature
on investigating social and geographical factors in recommender systems, it remains an open question how to leverage the intrinsic diversity of social data for optimally enhancing the living experiences of citizens.

This workshop on “Recommender Systems for Citizens” aims at bringing together researchers and practitioners from different disciplines to explore the challenges and opportunities of novel approaches to recommender systems that address the intrinsic diversity of social data as a core element of their scientific study, design principles, or implementations for improving citizen living experiences.

As the research and applications of recommender systems quickly grow, there is an increasing awareness and interest for recommender systems to expand their societal impact. Based on the recent success of related workshops, this workshop will enable an interdisciplinary consideration of the topic, combining perspectives from computer science, social science, citizen science, and urban science.

  • Jie Yang, Delft University of Technology, Netherlands
  • Zhu Sun, Nanyang Technological University, Singapore
  • Alessandro Bozzon, Delft University of Technology, Netherlands
  • Jie Zhang, Nanyang Technological University, Singapore
  • Martha Larson, Radboud University Nijmegen, Netherlands


Thursday, Aug 31, 2017, 14:00-17:30


Room 3

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