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.

推荐系统不仅成为用户的宝贵助手，因为用户可能会在大量不同的选择中迷失方向，而且对于服务提供商或卖家来说，他们希望能够引导客户选择特定的商品具有特定的特征。这种影响力在旅游领域特别有用，近年来，以更可持续的方式管理行业的需求以及预测和控制 PoI（兴趣点）拥挤程度的能力变得更加紧迫。在本文中，我们研究了上下文信息在确定 PoI 职业和用户偏好方面的作用，并探讨了机器学习和深度学习技术如何通过丰富历史信息及其上下文信息来帮助为用户提供良好的推荐。因此，我们提出了 ARTEMIS 的架构，这是一个具有拥挤预测功能的上下文感知推荐系统，能够根据历史上下文特征学习和预测用户偏好和职业水平。在整篇论文中，我们参考了 2014 年至 2019 年间在意大利北部城市维罗纳进行的游客访问的真实应用场景。