mlsanityData to 14 Oct 2026

Multi-Behavior Recommender Systems: A Survey

arXiv 2503.06963 ↗First posted 10 Mar 2025cs.IR cs.AIcited by 4

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Abstract (arXiv)

Traditional recommender systems primarily rely on a single type of user-item interaction, such as item purchases or ratings, to predict user preferences. However, in real-world scenarios, users engage in a variety of behaviors, such as clicking on items or adding them to carts, offering richer insights into their interests. Multi-behavior recommender systems leverage these diverse interactions to enhance recommendation quality, and research on this topic has grown rapidly in recent years. This survey provides a timely review of multi-behavior recommender systems, focusing on three key steps: (1) Data Modeling: representing multi-behaviors at the input level, (2) Encoding: transforming these inputs into vector representations (i.e., embeddings), and (3) Training: optimizing machine-learning models. We systematically categorize existing multi-behavior recommender systems based on the commonalities and differences in their approaches across the above steps. Additionally, we discuss promising future directions for advancing multi-behavior recommender systems.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 39 of its 66 references that are in the map. First posted on arXiv 10 Mar 2025.

Builds on

39 of 66 references.

References · 39

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,057
  3. 2020LightGCN: Simplifying and Powering Graph Convolution Network for Recommendationcited by 1,139
  4. 2019BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformercited by 964
  5. 2024Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative Recommendationscited by 188
  6. 2022Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learningcited by 86
  7. 2021Contrastive Learning for Cold-Start Recommendationcited by 79
  8. 2022Knowledge Graph Contrastive Learning for Recommendationcited by 70
  9. 2022XSimGCL: Towards Extremely Simple Graph Contrastive Learning for Recommendationcited by 57
  10. 2022Multi-Behavior Hypergraph-Enhanced Transformer for Sequential Recommendationcited by 42
  11. 2021Knowledge-Enhanced Hierarchical Graph Transformer Network for Multi-Behavior Recommendationcited by 39
  12. 2021Graph Meta Network for Multi-Behavior Recommendationcited by 38
27 more
  1. 2022Contrastive Meta Learning with Behavior Multiplicity for Recommendationcited by 37
  2. 2023Automated Self-Supervised Learning for Recommendationcited by 37
  3. 2021Multiplex Behavioral Relation Learning for Recommendation via Memory Augmented Transformer Networkcited by 28
  4. 2023Graph Transformer for Recommendationcited by 28
  5. 2018Learning from History and Present: Next-item Recommendation via Discriminatively Exploiting User Behaviorscited by 27
  6. 2022Disentangled Contrastive Learning for Social Recommendationcited by 27
  7. 2022CrossCBR: Cross-view Contrastive Learning for Bundle Recommendationcited by 26
  8. 2021Hyper Meta-Path Contrastive Learning for Multi-Behavior Recommendationcited by 21
  9. 2022Multi-view Multi-behavior Contrastive Learning in Recommendationcited by 21
  10. 2024END4Rec: Efficient Noise-Decoupling for Multi-Behavior Sequential Recommendationcited by 19
  11. 2023Knowledge Enhancement for Contrastive Multi-Behavior Recommendationcited by 16
  12. 2024Personalized Behavior-Aware Transformer for Multi-Behavior Sequential Recommendationcited by 15
  13. 2024Multi-Behavior Generative Recommendationcited by 15
  14. 2022Cascading Residual Graph Convolutional Network for Multi-Behavior Recommendationcited by 12
  15. 2023Multi-behavior Self-supervised Learning for Recommendationcited by 11
  16. 2023Multi-Behavior Recommendation with Cascading Graph Convolution Networkscited by 8
  17. 2019Collaborative Metric Learning with Memory Network for Multi-Relational Recommender Systemscited by 7
  18. 2022Multi-Behavior Enhanced Recommendation with Cross-Interaction Collaborative Relation Modelingcited by 7
  19. 2023Improving Implicit Feedback-Based Recommendation through Multi-Behavior Alignmentcited by 7
  20. 2023A Survey on Multi-Behavior Sequential Recommendationcited by 7
  21. 2022Coarse-to-Fine Knowledge-Enhanced Multi-Interest Learning Framework for Multi-Behavior Recommendationcited by 6
  22. 2023Parallel Knowledge Enhancement based Framework for Multi-behavior Recommendationcited by 6
  23. 2024Behavior-Contextualized Item Preference Modeling for Multi-Behavior Recommendationcited by 6
  24. 2023MB-HGCN: A Hierarchical Graph Convolutional Network for Multi-behavior Recommendationcited by 3
  25. 2024Multi-Behavior Collaborative Filtering with Partial Order Graph Convolutional Networkscited by 3
  26. 2024HMAR: Hierarchical Masked Attention for Multi-Behaviour Recommendationcited by 2
  27. 2021Criterion-based Heterogeneous Collaborative Filtering for Multi-behavior Implicit Recommendationcited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 2.

4 papers.

  1. 2025ItemRAG: Item-Based Retrieval-Augmented Generation for LLM-Based Recommendationcited by 2
  2. 2025A Self-Supervised Mixture-of-Experts Framework for Multi-behavior Recommendationcited by 0
  3. 2026AlphaFree: Recommendation Free from Users, IDs, and GNNscited by 0
  4. 2026Beyond Observed Auxiliary Relations: Environment-Conditioned Modeling for Multi-Behavior Recommendationcited by 0