ARTEMIS: a Context-Aware Recommendation System with Crowding Forecaster for the Touristic Domain

Abstract

Recommendation systems are becoming an invaluable assistant not only for users, who may be disoriented in the presence of a huge number of different alternatives, but also for service providers or sellers, who would like to be able to guide the choice of customers toward particular items with specific characteristics. This influence capability can be particularly useful in the tourism domain, where the need to manage the industry in a more sustainable way and the ability to predict and control the level of crowding of PoIs (Points of Interest) have become more pressing in recent years. In this paper, we study the role of contextual information in determining both PoI occupations and user preferences, and we explore how machine learning and deep learning techniques can help produce good recommendations for users by enriching historical information with its contextual counterpart. As a result, we propose the architecture of ARTEMIS, a context-Aware Recommender sysTEM wIth crowding forecaSting, able to learn and forecast user preferences and occupation levels based on historical contextual features. Throughout the paper, we refer to a real-world application scenario regarding the tourist visits performed in Verona, a municipality in Northern Italy, between 2014 and 2019.