Social Sensing for Personalized Orienteering Mediating the Need for Sociality and the Risk of COVID-19

• Published in: IEEE transactions on technology and society Vol. 3; no. 4; pp. 323 - 332
• Main Authors: Pilato, Giovanni, Persia, Fabio, Ge, Mouzhi, D'Auria, Daniela
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.12.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; contagion prevention; Coronaviruses; COVID-19; crowdedness avoidance; Modular systems; Orienteering; Planning; Preferences; Recommender systems; Routing; Sentiment analysis; Social computing; Social networking (online); Social sensing; Tourism industry; Tradeoffs; Traffic
• ISSN: 2637-6415; 2637-6415
• DOI: 10.1109/TTS.2022.3210882

Orienteering or itinerary planning applications aim to optimize travel routes exploiting user preference and other constraints, such as time budget or traffic conditions. For these algorithms, it is essential to explore the user preference to predict potential Points-Of-Interest (POI) or touristic routes. However, user preference has been significantly affected by the COVID-19, since health concern plays a key trade-off role now. For example, people may try to avoid crowdedness, even if there is a strong social desire. However, most orienteering applications just focus on user preferences, thus paying less attention to the variety of the data inputs, which has become an essential factor for the utility of the application in the COVID-19 era. Therefore, this paper proposes a social sensing system that considers the trade-off between user preference and various factors, such as crowdedness, fear of being infected, knowledge of the COVID-19, POI features, and desire for socialization. The experiments are conducted on profiling user interests with Doc2Vec and FastText based on the Yelp dataset. Furthermore, the proposed system is modular and can be efficiently adapted to different applications for COVID-aware itinerary planning.