  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.
