# Hyper Meta-Path Contrastive Learning for Multi-Behavior Recommendation

arXiv: https://arxiv.org/abs/2109.02859 · HTML: https://mlsanity.com/paper/2109.02859/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 35 references that are in the map.
- First posted on arXiv 7 Sep 2021.

## Builds on (most cited first, 23 of 23)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017)
- [Simplifying Graph Convolutional Networks](https://mlsanity.com/paper/1902.07153/) (2019)
- [Graph Contrastive Learning with Augmentations](https://mlsanity.com/paper/2010.13902/) (2020)
- [LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation](https://mlsanity.com/paper/2002.02126/) (2020)
- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer](https://mlsanity.com/paper/1904.06690/) (2019)
- [GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training](https://mlsanity.com/paper/2006.09963/) (2020)
- [Heterogeneous Information Network Embedding for Recommendation](https://mlsanity.com/paper/1711.10730/) (2017)
- [Topology Adaptive Graph Convolutional Networks](https://mlsanity.com/paper/1710.10370/) (2017)
- [Heterogeneous Hypergraph Embedding for Graph Classification](https://mlsanity.com/paper/2010.10728/) (2020)
- [Temporal Meta-path Guided Explainable Recommendation](https://mlsanity.com/paper/2101.01433/) (2021)
- [Meta-Graph: Few Shot Link Prediction via Meta Learning](https://mlsanity.com/paper/1912.09867/) (2019)
- [Where are we in embedding spaces? A Comprehensive Analysis on Network Embedding Approaches for Recommender Systems](https://mlsanity.com/paper/2105.08908/) (2021)
- [Hyperbolic Hypergraphs for Sequential Recommendation](https://mlsanity.com/paper/2108.08134/) (2021)
- [Learning to Recommend with Multiple Cascading Behaviors](https://mlsanity.com/paper/1809.08161/) (2018)
- [Heterogeneous Network Representation Learning: A Unified Framework with Survey and Benchmark](https://mlsanity.com/paper/2004.00216/) (2020)
- [HeteSim: A General Framework for Relevance Measure in Heterogeneous Networks](https://mlsanity.com/paper/1309.7393/) (2013)
- [Hyperbolic Neural Collaborative Recommender](https://mlsanity.com/paper/2104.07414/) (2021)
- [Interpretable Signed Link Prediction with Signed Infomax Hyperbolic Graph](https://mlsanity.com/paper/2011.12517/) (2020)

## Built on by (most cited first, 21 of 21)

- [Multi-Behavior Hypergraph-Enhanced Transformer for Sequential Recommendation](https://mlsanity.com/paper/2207.05584/) (2022)
- [Self-Supervised Hypergraph Transformer for Recommender Systems](https://mlsanity.com/paper/2207.14338/) (2022)
- [Unsupervised Graph Poisoning Attack via Contrastive Loss Back-propagation](https://mlsanity.com/paper/2201.07986/) (2022)
- [HICF: Hyperbolic Informative Collaborative Filtering](https://mlsanity.com/paper/2207.09051/) (2022)
- [Hyperbolic Representation Learning: Revisiting and Advancing](https://mlsanity.com/paper/2306.09118/) (2023)
- [Dual Space Graph Contrastive Learning](https://mlsanity.com/paper/2201.07409/) (2022)
- [SSLRec: A Self-Supervised Learning Framework for Recommendation](https://mlsanity.com/paper/2308.05697/) (2023)
- [Personalized Behavior-Aware Transformer for Multi-Behavior Sequential Recommendation](https://mlsanity.com/paper/2402.14473/) (2024)
- [HoPE: Hyperbolic Rotary Positional Encoding for Stable Long-Range Dependency Modeling in Large Language Models](https://mlsanity.com/paper/2509.05218/) (2025)
- [Hypergraph Self-supervised Learning with Sampling-efficient Signals](https://mlsanity.com/paper/2404.11825/) (2024)
- [A Comprehensive Survey on Self-Supervised Learning for Recommendation](https://mlsanity.com/paper/2404.03354/) (2024)
- [Dual-Channel Multiplex Graph Neural Networks for Recommendation](https://mlsanity.com/paper/2403.11624/) (2024)
- [Towards Open-world Cross-Domain Sequential Recommendation: A Model-Agnostic Contrastive Denoising Approach](https://mlsanity.com/paper/2311.04760/) (2023)
- [Multi-Behavior Recommender Systems: A Survey](https://mlsanity.com/paper/2503.06963/) (2025)
- [Learning Along the Arrow of Time: Hyperbolic Geometry for Backward-Compatible Representation Learning](https://mlsanity.com/paper/2506.05826/) (2025)
- [Mitigating the Performance Sacrifice in DP-Satisfied Federated Settings through Graph Contrastive Learning](https://mlsanity.com/paper/2207.11836/) (2022)
- [Hyperbolic Graph Representation Learning: A Tutorial](https://mlsanity.com/paper/2211.04050/) (2022)
- [Heterogeneous Causal Metapath Graph Neural Network for Gene-Microbe-Disease Association Prediction](https://mlsanity.com/paper/2406.19156/) (2024)
- [HypRAG: Hyperbolic Dense Retrieval for Retrieval Augmented Generation](https://mlsanity.com/paper/2602.07739/) (2026)
- [A Survey of Latent Factor Models in Recommender Systems](https://mlsanity.com/paper/2405.18068/) (2024)
- [A Self-Supervised Mixture-of-Experts Framework for Multi-behavior Recommendation](https://mlsanity.com/paper/2508.19507/) (2025)

## Abstract (arXiv)

User purchasing prediction with multi-behavior information remains a challenging problem for current recommendation systems. Various methods have been proposed to address it via leveraging the advantages of graph neural networks (GNNs) or multi-task learning. However, most existing works do not take the complex dependencies among different behaviors of users into consideration. They utilize simple and fixed schemes, like neighborhood information aggregation or mathematical calculation of vectors, to fuse the embeddings of different user behaviors to obtain a unified embedding to represent a user's behavioral patterns which will be used in downstream recommendation tasks. To tackle the challenge, in this paper, we first propose the concept of hyper meta-path to construct hyper meta-paths or hyper meta-graphs to explicitly illustrate the dependencies among different behaviors of a user. How to obtain a unified embedding for a user from hyper meta-paths and avoid the previously mentioned limitations simultaneously is critical. Thanks to the recent success of graph contrastive learning, we leverage it to learn embeddings of user behavior patterns adaptively instead of assigning a fixed scheme to understand the dependencies among different behaviors. A new graph contrastive learning based framework is proposed by coupling with hyper meta-paths, namely HMG-CR, which consistently and significantly outperforms all baselines in extensive comparison experiments.