Recommender systems (RSs) have been playing an increasingly important role for informed consumption, services, and decision-making in the overloaded information era and digitized economy. In recent years, session-based recommender systems (SBRSs) have emerged as a new paradigm of RSs. Different from other RSs such as content-based RSs and collaborative filtering-based RSs that usually model long-term yet static user preferences, SBRSs aim to capture short-term but dynamic user preferences to provide more timely and accurate recommendations sensitive to the evolution of their session contexts. Although SBRSs have been intensively studied, neither unified problem statements for SBRSs nor in-depth elaboration of SBRS characteristics and challenges are available. It is also unclear to what extent SBRS challenges have been addressed and what the overall research landscape of SBRSs is. This comprehensive review of SBRSs addresses the above aspects by exploring in depth the SBRS entities (e.g., sessions), behaviours (e.g., users’ clicks on items), and their properties (e.g., session length). We propose a general problem statement of SBRSs, summarize the diversified data characteristics and challenges of SBRSs, and define a taxonomy to categorize the representative SBRS research. Finally, we discuss new research opportunities in this exciting and vibrant area.

中文翻译：

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基于会话的推荐系统调查

在信息过载和数字化经济时代，推荐系统 (RS) 在知情消费、服务和决策方面发挥着越来越重要的作用。近年来，基于会话的推荐系统（SBRS）已经成为一种新的推荐系统范式。与其他 RS 不同，例如基于内容的 RS 和基于协同过滤的 RS，它们通常对长期但静态的用户偏好进行建模，SBRS 旨在捕捉短期但动态的用户偏好，以提供更及时、更准确的推荐，以适应用户的进化。他们的会话上下文。尽管对 SBRS 进行了深入研究，但既没有统一的 SBRS 问题陈述，也没有深入阐述 SBRS 的特点和挑战。也不清楚 SBRS 的挑战在多大程度上得到了解决，以及 SBRS 的整体研究前景如何。对 SBRS 的全面回顾通过深入探讨 SBRS 实体（例如会话）、行为（例如用户对项目的点击）及其属性（例如会话长度）来解决上述问题。我们提出了 SBRS 的一般问题陈述，总结了 SBRS 的多样化数据特征和挑战，并定义了一个分类法来对具有代表性的 SBRS 研究进行分类。最后，我们讨论了这个令人兴奋和充满活力的领域的新研究机会。我们提出了 SBRS 的一般问题陈述，总结了 SBRS 的多样化数据特征和挑战，并定义了一个分类法来对具有代表性的 SBRS 研究进行分类。最后，我们讨论了这个令人兴奋和充满活力的领域的新研究机会。我们提出了 SBRS 的一般问题陈述，总结了 SBRS 的多样化数据特征和挑战，并定义了一个分类法来对具有代表性的 SBRS 研究进行分类。最后，我们讨论了这个令人兴奋和充满活力的领域的新研究机会。