A Survey on Multi-Behavior Sequential Recommendation

TL;DR

This survey reviews multi-behavior sequential recommendation (MBSR), discussing its problem definition, classifications, methodologies, and future research directions to enhance recommendation accuracy.

Contribution

It provides a comprehensive classification and analysis of MBSR methods, including neighborhood, matrix factorization, and deep learning approaches, highlighting their strengths and weaknesses.

Findings

Deep learning methods dominate MBSR research.

Hybrid architectures improve recommendation performance.

Future directions include addressing challenges and exploring new models.

Abstract

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.