ACM RecSys 2017 Workshop
Recommender Systems for Citizens
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Overview

Citizens' RecSys: RecSys run by Citizens and 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.

Call for Papers

Recommender systems have been playing a central role in a variety of domains. When restricted to the application for citizens, a large body of literature could be found on Point-Of-Interest (POI) recommendation, tourist location recommendation, and orthogonally, spatio-temporal context-aware recommendation.

However, existing literature primarily focuses on a single domain (e.g. POI recommendation) from an algorithmic perspective, without considering from a personal perspective the closely related citizen activities as a continuous experience. Citizen activities in daily life, including transportation, working, sports, entertainment, shopping, etc., while being diverse, are closely related to each other. Together they describe a continuous experience for a citizen living in an urban environment. This calls for scientific investigation on the relationships among citizens' daily activities, in order to better understand their behaviors.

More importantly, the target user of conventional recommender systems are individuals, while citizens' recommender systems serve the society as a whole. To optimise the effectiveness of recommendations to the society, citizens' recommender systems require deep understanding of citizen-environment and citizen-citizen interactions. For example, to optimally recommend driving routes to a community of citizens, the system should be able to understand how the effectiveness of recommendations is influenced by the environment (e.g. road condition) and how the recommendations to different citizens affect each other (e.g. to avoid traffic congestion). The latter example shows the need for either algorithmic design for the recommendations to benefit the society as a whole, or incentive mechanisms to balance personal and societal interests.

The success of citizens' recommender systems heavily depends on the amount and quality of citizens' behavioural data, which were used to be scarce. The scarcity, on the one hand, can be compensated by engaging citizens to actively contribute their behavioural data to the system, i.e. citizen crowdsourcing via effective incentive schemes. On the other hand, the emergence of social data, i.e. data generated by people during their societal activities, available through new sources (e.g. social media, mobile phones, sensor networks), brings both opportunities and challenges to the development of citizens' recommender systems. Such data, when well-integrated, contain a multitude of dimensions, such as the targeted urban population, the purpose of use, the spatio-temporal context, etc.. Thus they describe comprehensively citizens' behaviors and their relationships with the environment, providing opportunities for recommendation based techniques to enhance citizens' living experiences. At the same time, social data are characterized by an intrinsic diversity, manifested through each of the relevant dimensions. It remains an open question how to leverage such diversity for optimally enhancing citizens' living experiences.

Recent studies have shown that recommender systems can actively change citizen mobility patterns, reducing traffic congestion and improve urban mobility. By exploiting social data and addressing the challenges, recommender systems have the potential to largely expand their impact and play an important role in today's society, in improving citizens' living experiences and the effectiveness of environmental use.

The topics of interest include but are not limited to:
  • Requirements definition, design and implementation for citizen recommendation
  • Collection, integration, exploration of social data for citizen recommendation
  • Citizen user modeling and behavioral analysis
  • Mining social data, social urban data for citizen recommendation
  • Crowdsourcing for citizen recommendation
  • Group recommendation in citizens' recommender systems
  • Algorithms for citizen recommendation
  • Incentivazation in citizen recommendation
  • Spatio-temporal context in citizen recommendation
  • Revisiting of POI recommendation in urban environment
  • Cross-domain recommendation for citizens' continuous living experiences
  • Citizen recommendation for smart urban environment
  • Design, implementation of citizen knowledge base, and knowledge transfer to citizen recommendation
  • User interface for citizen recommendation
  • Ethical, cultural issues related to citizen recommendation
  • Privacy and policy in citizen recommendation

Submissions Guidelines

The workshop accepts
  • regular research papers (4 to 6 pages), and
  • application/demonstration papers (2 pages)
  • position papers (2 pages)
in ACM conference format (references are counted in the page limit). All of the submissions should be submitted via EasyChair system: https://easychair.org/conferences/?conf=citrec2017.

After the workshop, the participants will come together to write a position paper about the potential and challenges of citizen recommendation.

The accepted papers and the collectively written position paper will be published in a workshop proceedings. We have already secured the funding necessary to be able to publish in the ICPS series of ACM Digital Library (i.e., the choice of RecSys Challenge and DLRS workshops in 2016).

Important Dates

June 22th, Paper Submission
July 13th, Acceptance Notification
July 27th, Camera-Ready Submission
August 27th, Workshop Date

Program Committee

Organizers

Send an email to citrec2017[at]gmail.com for questions.

Jie Yang

Delft University of Technology, Netherlands
j.yang-3[at]tudelft.nl

Zhu Sun

Nanyang Technological University, Singapore
sunzhu[at]ntu.edu.sg

Alessandro Bozzon

Delft University of Technology, Netherlands
a.bozzon[at]tudelft.nl

Jie Zhang

Nanyang Technological University, Singapore
zhangj[at]ntu.edu.sg

Martha Larson

Radboud University Nijmegen, Netherlands
m.a.larson[at]tudelft.nl