# Multi-Behavior Graph Neural Networks for Recommender System

arXiv: https://arxiv.org/abs/2302.08678 · HTML: https://mlsanity.com/paper/2302.08678/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Feb 2023.

## Built on by (most cited first, 9 of 9)

- [RecKG: Knowledge Graph for Recommender Systems](https://mlsanity.com/paper/2501.03598/) (2025)
- [Variational Disentangled Graph Auto-Encoders for Link Prediction](https://mlsanity.com/paper/2306.11315/) (2023)
- [Out-of-Distribution Generalization on Graphs via Progressive Inference](https://mlsanity.com/paper/2503.02988/) (2025)
- [Heterogeneous Graph Prompt Learning via Adaptive Weight Pruning](https://mlsanity.com/paper/2507.09132/) (2025)
- [Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks](https://mlsanity.com/paper/2509.07499/) (2025)
- [Robust Representation Learning for Unified Online Top-K Recommendation](https://mlsanity.com/paper/2310.15492/) (2023)
- [Knowledge-Aware Multi-Intent Contrastive Learning for Multi-Behavior Recommendation](https://mlsanity.com/paper/2404.11993/) (2024)
- [Dynamic Graph with Similarity-Aware Attention Graph Neural Network for Recommender Systems](https://mlsanity.com/paper/2605.05238/) (2026)
- [HiSE: A Lightweight Hierarchical Semantic Explainer for Heterogeneous Graph Neural Networks](https://mlsanity.com/paper/2606.03495/) (2026)

## Abstract (arXiv)

Recommender systems have been demonstrated to be effective to meet user's personalized interests for many online services (e.g., E-commerce and online advertising platforms). Recent years have witnessed the emerging success of many deep learning-based recommendation models for augmenting collaborative filtering architectures with various neural network architectures, such as multi-layer perceptron and autoencoder. However, the majority of them model the user-item relationship with single type of interaction, while overlooking the diversity of user behaviors on interacting with items, which can be click, add-to-cart, tag-as-favorite and purchase. Such various types of interaction behaviors have great potential in providing rich information for understanding the user preferences. In this paper, we pay special attention on user-item relationships with the exploration of multi-typed user behaviors. Technically, we contribute a new multi-behavior graph neural network (MBRec), which specially accounts for diverse interaction patterns as well as the underlying cross-type behavior inter-dependencies. In the MBRec framework, we develop a graph-structured learning framework to perform expressive modeling of high-order connectivity in behavior-aware user-item interaction graph. After that, a mutual relation encoder is proposed to adaptively uncover complex relational structures and make aggregations across layer-specific behavior representations. Through comprehensive evaluation on real-world datasets, the advantages of our MBRec method have been validated under different experimental settings. Further analysis verifies the positive effects of incorporating the multi-behavioral context into the recommendation paradigm. Additionally, the conducted case studies offer insights into the interpretability of user multi-behavior representations.