Iván Cantador
Alejandro Bellogín
ABSTRACT
In e-participation platforms, citizens suggest, discuss and vote online for initiatives aimed to address a wide range of issues and problems in a city, such as economic development, public safety, budges, infrastructure, housing, environment, social rights, and health care. For a particular citizen, the number of proposals and debates may be overwhelming, and recommender systems could help filtering and ranking those that are more relevant. Focusing on a particular case, the `Decide Madrid' platform, in this paper we empirically investigate which sources of user preferences and recommendation approaches could be more effective, in terms of several aspects, namely precision, coverage and diversity.
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