Abstract
Different recommender systems suggest points of interest (POIs) based on data shared through geo-social networks (GSN). These systems are a very useful resource for mobile users, and an important business opportunity for advertisers. However, GSN data (e.g., the check-in of a person in a particular place) may be private information that a user may not want to release outside her social network. Even if the GSN service is trusted, and users' data is not directly released, an adversary may be able to reconstruct the data of a GSN user by mining the received recommendations. In this demo we will illustrate an implementation of the POI-Ti-Dico platform for privacy-conscious geo-social recommendation of POIs. The platform includes a server-side private recommender system and a mobile application for the Android framework. Recommendations are computed using a very large dataset of real check-ins.
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