STS dataset was collected by a context-aware recommender system mobile app named as "South Tyrol Suggests". The app provides context-aware recommendations for attractions, events, public services, restaurants, and much more based on the rating preferences and personality factors of users. Contextual variables includes distance: far away, near by time available: half day, one day, more than one day temperature: burning, hot, warm, cool, cold, freezing crowdedness: crowded, not crowded, empty knowledge of surroundings: new to area, returning visitor, citizen of the area season: spring, summer, autumn, winter budget: budget traveler, price for quality, high spender daytime: morning, noon, afternoon, evening, night weather: clear sky, sunny, cloudy, rainy, thunderstorm, snowing companion: alone, with friends/colleagues, with family, with girlfriend/boyfriend, with children mood: happy, sad, active, lazy weekday: weekday, weekend travel goal: visiting friends, business, religion, health care, social event, education, scenic/landscape, hedonistic/fun, activity/sport means of transport: no transportation means, a bicycle, a car, public transport More details can be found here: Braunhofer, Matthias, Mehdi Elahi, and Francesco Ricci. "Techniques for cold-starting context-aware mobile recommender systems for tourism." Intelligenza Artificiale 8, no. 2 (2014): 129-143.

{"references": ["Braunhofer, Matthias, Mehdi Elahi, and Francesco Ricci. \"Techniques for cold-starting context-aware mobile recommender systems for tourism.\" Intelligenza Artificiale 8, no. 2 (2014): 129-143.", "Elahi, Mehdi, Matthias Braunhofer, Francesco Ricci, and Marko Tkalcic. \"Personality-based active learning for collaborative filtering recommender systems.\" In Congress of the Italian Association for Artificial Intelligence, pp. 360-371. Springer, Cham, 2013.", "Elahi, Mehdi. \"Empirical Evaluation of Active Learning Strategies in Collaborative Filtering.\"", "Braunhofer, Matthias, Mehdi Elahi, and Francesco Ricci. \"STS: A Context-Aware Mobile Recommender System for Places of Interest.\" In UMAP Workshops. 2014.", "Braunhofer, Matthias, Mehdi Elahi, Mouzhi Ge, Francesco Ricci, and Thomas Schievenin. \"STS: Design of Weather-Aware Mobile Recommender Systems in Tourism.\" In AI* HCI@ AI* IA. 2013."]}