Abstract
We focus on personal data generated by the sensors and through the everyday usage of smart devices and take advantage of these data to build a non-invasive contextual suggestion system for tourism. The system, which we call Pythia, exploits the computational capabilities of modern smart devices to offer high quality personalized POI (point of interest) recommendations. To protect user privacy, we apply a privacy by design approach within all of the steps of creating Pythia. The outcome is a system that comprises important architectural and operational innovations. The system is designed to process sensitive personal data, such as location traces, browsing history and web searches (query logs), to automatically infer user preferences and build corresponding POI-based user profiles. These profiles are then used by a contextual suggestion engine to anticipate user choices and make POI recommendations for tourists. Privacy leaks are minimized by implementing an important part of the system functionality at the user side, either as a mobile app or as a client-side web application, and by taking additional precautions, like data generalization, wherever necessary. As a proof of concept, we present a prototype that implements the aforementioned mechanisms on the Android platform accompanied with certain web applications. Even though the current prototype focuses only on location data, the results from the evaluation of the contextual suggestion algorithms and the user experience feedback from volunteers who used the prototype are very positive.
Highlights
Smart devices, and in particular smartphones, are ubiquitous devices much more than communication tools, as they combine features of a cell phone along with computing functionalities
We focus on the sets of personal data of smart devices and consider how they can be used to make touristic contextual suggestions to their owners
In case the GPS provider fails to coordinate, the location is requested from the network provider
Summary
In particular smartphones, are ubiquitous devices much more than communication tools, as they combine features of a cell phone along with computing functionalities. The presented system innovates in several fronts by design and implementation choices to guarantee non-invasiveness and privacy preservation We consider this combination of features, which, to our knowledge, is unique in the field of mobile recommendation systems, the main novelty of our work. Rich user profiles: As the profiles and the contextual suggestion engine reside on the user’s mobile device, the system has access to his or her digital trace, which comprises a virtually unbounded, in size and type, set of personal data. Note that while the contextual suggestion engine has access to the local user profile, which may contain a very rich set of personal data, the suggestion algorithms base their computations only on a single profile, in contrast to collaborative filtering approaches.
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