# A Survey on Multi-Behavior Sequential Recommendation

arXiv: https://arxiv.org/abs/2308.15701 · HTML: https://mlsanity.com/paper/2308.15701/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Aug 2023.

## Built on by (most cited first, 7 of 7)

- [Multi-Modal Multi-Behavior Sequential Recommendation with Conditional Diffusion-Based Feature Denoising](https://mlsanity.com/paper/2508.05352/) (2025)
- [LettinGo: Explore User Profile Generation for Recommendation System](https://mlsanity.com/paper/2506.18309/) (2025)
- [Multi-Behavior Recommender Systems: A Survey](https://mlsanity.com/paper/2503.06963/) (2025)
- [DELRec: Distilling Sequential Pattern to Enhance LLMs-based Sequential Recommendation](https://mlsanity.com/paper/2406.11156/) (2024)
- [Compositions of Variant Experts for Integrating Short-Term and Long-Term Preferences](https://mlsanity.com/paper/2506.23170/) (2025)
- [CRAMER: Control via Request-Aware Masking for Editing Recommenders](https://mlsanity.com/paper/2608.25370/) (2026)
- [Behavior2Trip: Towards Personalized Travel Planning via User Behavior Trajectory](https://mlsanity.com/paper/2608.26807/) (2026)

## Abstract (arXiv)

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.