# Multi-view Multi-behavior Contrastive Learning in Recommendation

arXiv: https://arxiv.org/abs/2203.10576 · HTML: https://mlsanity.com/paper/2203.10576/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 44 references that are in the map.
- First posted on arXiv 20 Mar 2022.

## Builds on (most cited first, 30 of 30)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation](https://mlsanity.com/paper/2002.02126/) (2020)
- [Colorful Image Colorization](https://mlsanity.com/paper/1603.08511/) (2016)
- [Unsupervised Visual Representation Learning by Context Prediction](https://mlsanity.com/paper/1505.05192/) (2015)
- [BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer](https://mlsanity.com/paper/1904.06690/) (2019)
- [Session-based Recommendations with Recurrent Neural Networks](https://mlsanity.com/paper/1511.06939/) (2015)
- [Neural Graph Collaborative Filtering](https://mlsanity.com/paper/1905.08108/) (2019)
- [Deep Interest Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1706.06978/) (2017)
- [S^3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization](https://mlsanity.com/paper/2008.07873/) (2020)
- [Self-supervised Graph Learning for Recommendation](https://mlsanity.com/paper/2010.10783/) (2020)
- [Embedding-based Retrieval in Facebook Search](https://mlsanity.com/paper/2006.11632/) (2020)
- [Deep Subdomain Adaptation Network for Image Classification](https://mlsanity.com/paper/2106.09388/) (2021)
- [Warm Up Cold-start Advertisements: Improving CTR Predictions via Learning to Learn ID Embeddings](https://mlsanity.com/paper/1904.11547/) (2019)
- [ATRank: An Attention-Based User Behavior Modeling Framework for Recommendation](https://mlsanity.com/paper/1711.06632/) (2017)
- [Modeling the Sequential Dependence among Audience Multi-step Conversions with Multi-task Learning in Targeted Display Advertising](https://mlsanity.com/paper/2105.08489/) (2021)
- [Knowledge-Enhanced Hierarchical Graph Transformer Network for Multi-Behavior Recommendation](https://mlsanity.com/paper/2110.04000/) (2021)
- [Graph Meta Network for Multi-Behavior Recommendation](https://mlsanity.com/paper/2110.03969/) (2021)
- [Learning to Warm Up Cold Item Embeddings for Cold-start Recommendation with Meta Scaling and Shifting Networks](https://mlsanity.com/paper/2105.04790/) (2021)
- [Personalized Transfer of User Preferences for Cross-domain Recommendation](https://mlsanity.com/paper/2110.11154/) (2021)
- [Contrastive Cross-domain Recommendation in Matching](https://mlsanity.com/paper/2112.00999/) (2021)
- [Transfer-Meta Framework for Cross-domain Recommendation to Cold-Start Users](https://mlsanity.com/paper/2105.04785/) (2021)
- [Beyond Clicks: Modeling Multi-Relational Item Graph for Session-Based Target Behavior Prediction](https://mlsanity.com/paper/2002.07993/) (2020)
- [Multi-Representation Adaptation Network for Cross-domain Image Classification](https://mlsanity.com/paper/2201.01002/) (2022)
- [UPRec: User-Aware Pre-training for Recommender Systems](https://mlsanity.com/paper/2102.10989/) (2021)
- [Price-aware Recommendation with Graph Convolutional Networks](https://mlsanity.com/paper/2003.03975/) (2020)
- [Knowledge Transfer via Pre-training for Recommendation: A Review and Prospect](https://mlsanity.com/paper/2009.09226/) (2020)
- [Long Short-Term Temporal Meta-learning in Online Recommendation](https://mlsanity.com/paper/2105.03686/) (2021)
- [Neural Hierarchical Factorization Machines for User's Event Sequence Analysis](https://mlsanity.com/paper/2112.15292/) (2021)

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

- [Memory-Guided Multi-View Multi-Domain Fake News Detection](https://mlsanity.com/paper/2206.12808/) (2022)
- [Knowledge Enhancement for Contrastive Multi-Behavior Recommendation](https://mlsanity.com/paper/2301.05403/) (2023)
- [Multi-Modal Multi-Behavior Sequential Recommendation with Conditional Diffusion-Based Feature Denoising](https://mlsanity.com/paper/2508.05352/) (2025)
- [Scaling Sequential Recommendation Models with Transformers](https://mlsanity.com/paper/2412.07585/) (2024)
- [A Comprehensive Survey on Self-Supervised Learning for Recommendation](https://mlsanity.com/paper/2404.03354/) (2024)
- [Tricolore: Multi-Behavior User Profiling for Enhanced Candidate Generation in Recommender Systems](https://mlsanity.com/paper/2505.02120/) (2025)
- [Dual-Channel Multiplex Graph Neural Networks for Recommendation](https://mlsanity.com/paper/2403.11624/) (2024)
- [Multi-Behavior Recommender Systems: A Survey](https://mlsanity.com/paper/2503.06963/) (2025)
- [Denoising Pre-Training and Customized Prompt Learning for Efficient Multi-Behavior Sequential Recommendation](https://mlsanity.com/paper/2408.11372/) (2024)
- [Impression-Informed Multi-Behavior Recommender System: A Hierarchical Graph Attention Approach](https://mlsanity.com/paper/2309.03169/) (2023)
- [Thyroid ultrasound diagnosis improvement via multi-view self-supervised learning and two-stage pre-training](https://mlsanity.com/paper/2402.11497/) (2024)
- [Incorporating Heterogeneous User Behaviors and Social Influences for Predictive Analysis](https://mlsanity.com/paper/2207.11776/) (2022)
- [BMLP: Behavior-aware MLP for Heterogeneous Sequential Recommendation](https://mlsanity.com/paper/2402.12733/) (2024)
- [DFGNN: Dual-frequency Graph Neural Network for Sign-aware Feedback](https://mlsanity.com/paper/2405.15280/) (2024)
- [Residual Multi-Task Learner for Applied Ranking](https://mlsanity.com/paper/2411.09705/) (2024)
- [AdaF^2M^2: Comprehensive Learning and Responsive Leveraging Features in Recommendation System](https://mlsanity.com/paper/2501.15816/) (2025)
- [Knowledge-Aware Multi-Intent Contrastive Learning for Multi-Behavior Recommendation](https://mlsanity.com/paper/2404.11993/) (2024)
- [Empowering Contrastive Federated Sequential Recommendation with LLMs](https://mlsanity.com/paper/2602.09306/) (2026)
- [ID and Graph View Contrastive Learning with Multi-View Attention Fusion for Sequential Recommendation](https://mlsanity.com/paper/2604.14114/) (2026)
- [Multi-Level Graph Attention Network Contrastive Learning for Knowledge-Aware Recommendation](https://mlsanity.com/paper/2605.08499/) (2026)
- [User Representation via Cross Multi-source Behavior Pre-training for Mobile Games](https://mlsanity.com/paper/2609.01057/) (2026)

## Abstract (arXiv)

Multi-behavior recommendation (MBR) aims to jointly consider multiple behaviors to improve the target behavior's performance. We argue that MBR models should: (1) model the coarse-grained commonalities between different behaviors of a user, (2) consider both individual sequence view and global graph view in multi-behavior modeling, and (3) capture the fine-grained differences between multiple behaviors of a user. In this work, we propose a novel Multi-behavior Multi-view Contrastive Learning Recommendation (MMCLR) framework, including three new CL tasks to solve the above challenges, respectively. The multi-behavior CL aims to make different user single-behavior representations of the same user in each view to be similar. The multi-view CL attempts to bridge the gap between a user's sequence-view and graph-view representations. The behavior distinction CL focuses on modeling fine-grained differences of different behaviors. In experiments, we conduct extensive evaluations and ablation tests to verify the effectiveness of MMCLR and various CL tasks on two real-world datasets, achieving SOTA performance over existing baselines. Our code will be available on urlhttps://github.com/wyqing20/MMCLR