Cicerone: Design of a Real-Time Area Knowledge-Enhanced Venue Recommender

Abstract

Smart-devices with information sharing capabilities anytime and anywhere have opened a wide range of ubiquitous applications. Within urban environments citizens have a plethora of locations to choose from, and in the advent of the smart-cities paradigm, this is the scope of location-based recommender systems to provide citizens with the adequate suggestions. In this work we present the design of an in-situ location-based recommender system, where the venue recommendations are built upon the users ’ location at request-time, but also incorporating the social dimension and the expertise of the neighboring users knowledge used to build the recommendations. Moreover, we propose a specific easy-to-deploy architecture, that bases its functioning in the participatory social media platforms such as Twitter or Foursquare. Our system constructs its knowledge base from the accesible data in Foursquare, and similarly obtains ratings from geopositioned tweets. 1