A Privacy-by-Design Contextual Suggestion System for Tourism

Abstract

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.

Systematic Review of Contextual Suggestion and Recommendation Systems for Sustainable e-Tourism

“…Contextual suggestion system is a specific stream for research in e-tourism [11,; therefore, articles published from 2012 to May 2020 are used in this review. ii.…”

“…The categories of articles were decomposed into several categories from the three main classes, namely, review articles [40,[43][44][45][46][47][48][49], model/framework, and application. Under the second category of model/framework, it is sub-categorised into time-based [50][51][52][53], activity-based [17,[54][55][56][57][58][59][60][61][62][63][64][65][66], location-based [1,11,12,17,20,30,42,48,, social-based [9,[89][90][91][92][93][94][95][96][97][98][99][100][101][102][1...…”

“…In reality, other users are also searching for POIs that are near enough to their current location [77,78]. Only those users that are located close to GPS trajectories form their travel experiences pattern [11,17,20,[79][80][81]. The authors of [82] utilized the POIs' popularity to optimize the probability estimate of the relative distance between the recipient and the POI, taken from semantic tags.…”

“…The approaches in these systems fall in the area of information retrieval (IR) and artificial intelligence (AI). Based on these concepts, these systems can facilitate the tourists via providing a list of venues based on their context and personal preferences [11]. Tourists depend heavily on their cell phones when searching for events to participate in and finding fascinating nearby places or activities [12].…”

Enabling Responsible Online Gambling by Real-time Persuasive Technologies

“…Accordingly, the intelligent end-user agent, based on the current technological developments, could be provided as a mobile application or even a client-side web application (an example of both approaches is presented in [72]). The usage of smartphones as a platform to serve the intelligent agent through a mobile application could be the best option, because it is a well-known and powerful computing platform, is continuously together with the gambler, and embeds multiple sensors.…”