Recommender systems is set up to address the issue of information overload in traditional information retrieval systems, which is focused on recommending information that is of most interest to users from massive information. Generally, there is a sequential nature and heterogeneity to the behavior of a person interacting with a system, leading to the proposal of multi-behavior sequential recommendation (MBSR). MBSR is a relatively new and worthy direction for in-depth research, which can achieve state-of-the-art recommendation through suitable modeling, and some related works have been proposed. This survey aims to shed light on the MBSR problem. Firstly, we introduce MBSR in detail, including its problem definition, application scenarios and challenges faced. Secondly, we detail the classification of MBSR, including neighborhood-based methods, matrix factorization-based methods and deep learning-based methods, where we further classify the deep learning-based methods into different learning architectures based on RNN, GNN, Transformer, and generic architectures as well as architectures that integrate hybrid techniques. In each method, we present related works based on the data perspective and the modeling perspective, as well as analyze the strengths, weaknesses and features of these works. Finally, we discuss some promising future research directions to address the challenges and improve the current status of MBSR.

翻译：推荐系统旨在解决传统信息检索系统中的信息过载问题，其核心是从海量信息中为用户推荐最感兴趣的内容。用户与系统交互的行为通常具有序列性和异质性，由此催生了多行为序列推荐（MBSR）的研究。MBSR是一个较新且值得深入探索的方向，通过合适的建模即可实现最先进的推荐效果，目前已有部分相关研究工作。本综述旨在阐明MBSR问题。首先，我们详细介绍了MBSR，包括其问题定义、应用场景及面临的挑战。其次，我们详述了MBSR的分类方法，包括基于邻域的方法、基于矩阵分解的方法和基于深度学习的方法，其中将基于深度学习的方法进一步划分为基于RNN、GNN、Transformer等不同学习架构，以及通用架构和集成混合技术的架构。针对每种方法，我们从数据视角和建模视角梳理了相关研究工作，分析了这些工作的优势、劣势及特点。最后，我们讨论了若干有前景的未来研究方向，以应对当前挑战并改善MBSR的研究现状。