Recommender systems for citizens: the CitRec'17 workshop manifesto

This manifesto summarises the outcomes of the 1st Workshop on Recommender Systems for Citizens (CitRec'17), held at the 11th ACM Conference on Recommender Systems, in August 2017 in Como, Italy. We discuss challenges and opportunities for the development of recommender systems for citizens, including: the clarification of the role of recommender systems for cities and citizens; in this context, the identification of classes of items to be recommended; the need for targeting and engaging the right population, involving the right stakeholders; and the existence of underlying ethical issues such as fairness and consensus. We further provide an action plan to bring forward the research and application of recommender systems for citizens.

[1]  Jérôme Gensel,et al.  Users psychological profiles for leisure activity recommendation: user study , 2017, CitRec@RecSys.

[2]  S. Arnstein,et al.  Ladder of Citizen Participation , 2020 .

[3]  Iván Cantador,et al.  Recommender systems for e-governance in smart cities: state of the art and research opportunities , 2017, CitRec@RecSys.

[4]  Yong Liu,et al.  Your neighbors affect your ratings: on geographical neighborhood influence to rating prediction , 2014, SIGIR.

[5]  Alejandro Bellogín,et al.  Personalized recommendations in e-participation: offline experiments for the 'Decide Madrid' platform , 2017, CitRec@RecSys.

[6]  Daniel Gatica-Perez,et al.  SenseCityVity: Mobile Crowdsourcing, Urban Awareness, and Collective Action in Mexico , 2017, IEEE Pervasive Computing.

[7]  Yehuda Koren,et al.  Collaborative filtering with temporal dynamics , 2009, KDD.

[8]  Aniket Kittur,et al.  Organizing without formal organization: group identification, goal setting and social modeling in directing online production , 2012, CSCW.

[9]  Xing Xie,et al.  Learning travel recommendations from user-generated GPS traces , 2011, TIST.

[10]  Jennifer E. Rowley,et al.  e-Government stakeholders - Who are they and what do they want? , 2011, Int. J. Inf. Manag..

[11]  Fran Casino,et al.  Context-aware recommender for smart health , 2015, 2015 IEEE First International Smart Cities Conference (ISC2).

[12]  Dietmar Jannach,et al.  Recommendations with a Purpose , 2016, RecSys.

[13]  Jérôme Gensel,et al.  Towards matching improvement between spatio-temporal tasks and workers in mobile crowdsourcing market systems , 2014, MobiGIS '14.

[14]  M. Seligman,et al.  Orientations to happiness and life satisfaction: the full life versus the empty life , 2005 .

[15]  Lionel Brunie,et al.  Efficient Worker Selection Through History-Based Learning in Crowdsourcing , 2017, 2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC).

[16]  Mao Ye,et al.  Exploiting geographical influence for collaborative point-of-interest recommendation , 2011, SIGIR.

[17]  Archan Misra,et al.  Towards City-Scale Mobile Crowdsourcing: Task Recommendations under Trajectory Uncertainties , 2015, IJCAI.

[18]  Ben Richardson,et al.  The Hedonic and Eudaimonic Validity of the Orientations to Happiness Scale , 2014 .