Personalized recommendations have become a common feature of modern online services, including most major e-commerce sites, media platforms and social networks. Today, due to their high practical relevance, research in the area of recommender systems is flourishing more than ever. However, with the new application scenarios of recommender systems that we observe today, constantly new challenges arise as well, both in terms of algorithmic requirements and with respect to the evaluation of such systems. In this paper, we first provide an overview of the traditional formulation of the recommendation problem. We then review the classical algorithmic paradigms for item retrieval and ranking and elaborate how such systems can be evaluated. Afterwards, we discuss a number of recent developments in recommender systems research, including research on session-based recommendation, biases in recommender systems, and questions regarding the impact and value of recommender systems in practice.

翻译：个性化推荐已成为现代在线服务的常见功能，包括大多数主流电商网站、媒体平台和社交网络。如今，由于其实践价值极高，推荐系统领域的研究比以往任何时候都更加活跃。然而，随着当前推荐系统应用场景的不断涌现，无论是在算法需求方面，还是在对这类系统的评估方面，都持续出现新的挑战。本文首先概述推荐问题的传统表述方式，进而回顾经典的物品检索与排序算法范式，并阐述如何对这类系统进行评估。随后，我们讨论推荐系统研究中的一些最新进展，包括基于会话的推荐、推荐系统中的偏差，以及推荐系统在实际应用中的影响与价值等相关问题。