Recent years have witnessed the remarkable success of recommendation systems (RSs) in alleviating the information overload problem. As a new paradigm of RSs, session-based recommendation (SR) specializes in users’ short-term preferences and aims to provide a more dynamic and timely recommendation based on ongoing interactions. This survey presents a comprehensive overview of the recent works on SR. First, we clarify the key definitions within SR and compare the characteristics of SR against other recommendation tasks. Then, we summarize the existing methods in two categories: sequential neural network based methods and graph neural network (GNN) based methods. The relevant frameworks and technical details are further introduced. Finally, we discuss the challenges of SR and new research directions in this area.

中文翻译：

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基于会话的推荐中的图和顺序神经网络：一项调查


近年来，推荐系统 （RS） 在缓解信息过载问题方面取得了显著的成功。作为 RS 的一种新范式，基于会话的推荐 （SR） 专注于用户的短期偏好，旨在根据正在进行的交互提供更动态、更及时的推荐。本调查全面概述了 SR 的最新工作。首先，我们阐明了 SR 中的关键定义，并将 SR 的特征与其他推荐任务进行了比较。然后，我们将现有方法归纳为两类：基于序列神经网络的方法和基于图神经网络 （GNN） 的方法。将进一步介绍相关框架和技术细节。最后，我们讨论了 SR 的挑战和该领域的新研究方向。