Skip to main content
Springer Nature Link
Log in

A survey on multi-behavior sequential recommendation

  • Review
  • Published:
Save article
View saved research
Science China Information Sciences Aims and scope Submit manuscript

Abstract

People usually have the explicit or implicit desire to get the information they need and are most interested in from massive information, which has led to the creation of personalized recommender systems. Recommender systems are set up to address the issue of information overload in traditional information retrieval systems such as search engines, and have been a significant area of research focusing on recommending information that is of most interest to users. There is a sequential nature to the behaviors of a person interacting with a system, such as examining one item of clothing before examining others. The problem of taking this sequential nature into account to uncover users’ interest dynamics in delivering recommendations is known as sequential recommendation (SR). The traditional SR problem merely focuses on a single behavior type of the users, while in real-world scenarios, users tend to engage in multiple types of behaviors, such as examining and adding clothes to the cart before purchasing them. The introduction of multiple behavior types can uncover users’ behavior patterns more comprehensively, leading to the proposal of multi-behavior sequential recommendation (MBSR). MBSR considers both sequentiality and heterogeneity of user behaviors, which can achieve state-of-the-art recommendation performance through suitable modeling. Currently, there are some related studies for MBSR, and to the best of our knowledge, there is no related review to introduce and categorize these MBSR studies. Hence, this survey aims to shed light on MBSR, which is a relatively new and worthy direction for in-depth research. First, we introduce MBSR in detail, including its problem definition, application scenarios, and challenges faced. Second, we detail the classification of MBSR methods, including traditional methods and deep learning-based methods, where the former contain neighborhood-based methods and matrix factorization-based methods, and the latter can be classified into different learning architectures based on recurrent neural network (RNN), graph neural network (GNN), Transformer, and generic architectures, as well as architectures that integrate hybrid techniques. In each method, we present related studies from the data perspective and the modeling perspective, analyzing the strengths, weaknesses, and features of these studies, and further conduct experiments on two real-world datasets with classical and recent studies on different methods to show the difference in recommendation performance of these methods. Finally, we discuss some promising future research directions to address the challenges and improve the current status of MBSR.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+
from $39.99 /Month
  • Starting from 10 chapters or articles per month
  • Access and download chapters and articles from more than 300k books and 2,500 journals
  • Cancel anytime
View plans

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Explore related subjects

Discover the latest articles, books and news in related subjects, suggested using machine learning.

References

  1. Adomavicius G, Tuzhilin A. Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans Knowl Data Eng, 2005, 17: 734–749

    Article  Google Scholar 

  2. Ricci F, Rokach L, Shapira B. Recommender Systems Handbook. 2nd ed. New York: Springer, 2015

    Book  Google Scholar 

  3. Gai L, Lei L. Dual collaborative topic modeling from implicit feedbacks. In: Proceedings of the 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics, 2014. 395–404

    Google Scholar 

  4. Li G, Ou W. Pairwise probabilistic matrix factorization for implicit feedback collaborative filtering. Neurocomputing, 2016, 204: 17–25

    Article  Google Scholar 

  5. Rendle S, Freudenthaler C, Gantner Z, et al. BPR: Bayesian personalized ranking from implicit feedback. In: Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, 2009. 452–461

    Google Scholar 

  6. Pan W, Zhong H, Xu C, et al. Adaptive Bayesian personalized ranking for heterogeneous implicit feedbacks. Knowl-Based Syst, 2015, 73: 173–180

    Article  Google Scholar 

  7. Qiu H, Liu Y, Guo G, et al. BPRH: Bayesian personalized ranking for heterogeneous implicit feedback. Inform Sci, 2018, 453: 80–98

    Article  Google Scholar 

  8. Tang L, Long B, Chen B, et al. An empirical study on recommendation with multiple types of feedback. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016. 283–292

    Chapter  Google Scholar 

  9. Pan W, Liu M, Ming Z. Transfer learning for heterogeneous one-class collaborative filtering. IEEE Intell Syst, 2016, 31: 43–49

    Article  Google Scholar 

  10. Pan W, Yang Q, Cai W, et al. Transfer to rank for heterogeneous one-class collaborative filtering. ACM Trans Inf Syst, 2019, 37: 1–20

    Article  Google Scholar 

  11. Rendle S, Freudenthaler C, Schmidt-Thieme L. Factorizing personalized Markov chains for next-basket recommendation. In: Proceedings of the 19th International Conference on World Wide Web, 2010. 811–820

    Chapter  Google Scholar 

  12. Zeng Z, Lin J, Li L, et al. Next-item recommendation via collaborative filtering with bidirectional item similarity. ACM Trans Inf Syst, 2019, 38: 1–22

    Article  Google Scholar 

  13. Tang J, Wang K. Personalized top-n sequential recommendation via convolutional sequence embedding. In: Proceedings of the 11th ACM International Conference on Web Search and Data Mining, 2018. 565–573

    Google Scholar 

  14. Hidasi B, Karatzoglou A, Baltrunas L, et al. Session-based recommendations with recurrent neural networks. In: Proceedings of the 4th International Conference on Learning Representations, 2016

    Google Scholar 

  15. Kang W C, McAuley J. Self-attentive sequential recommendation. In: Proceedings of the 2018 IEEE International Conference on Data Mining, 2018. 197–206

    Google Scholar 

  16. Liu Q, Wu S, Wang L. Multi-behavioral sequential prediction with recurrent log-bilinear model. IEEE Trans Knowl Data Eng, 2017, 29: 1254–1267

    Article  Google Scholar 

  17. Meng W, Yang D, Xiao Y. Incorporating user micro-behaviors and item knowledge into multi-task learning for session-based recommendation. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2020. 1091–1100

    Chapter  Google Scholar 

  18. Yuan E, Guo W, He Z, et al. Multi-behavior sequential transformer recommender. In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2022. 1642–1652

    Chapter  Google Scholar 

  19. Fang H, Zhang D, Shu Y, et al. Deep learning for sequential recommendation. ACM Trans Inf Syst, 2020, 39: 1–42

    Article  Google Scholar 

  20. Chen S, Xu Z, Pan W, et al. A survey on cross-domain sequential recommendation. ArXiv:2401.04971

  21. Chen X, Lin L, Pan W, et al. A survey on heterogeneous one-class collaborative filtering. ACM Trans Inform Syst, 2020, 38: 1–54

    Google Scholar 

  22. Smith B, Linden G. Two decades of recommender systems at Amazon.com. IEEE Internet Comput, 2017, 21: 12–18

    Article  Google Scholar 

  23. Wang J, Huang P, Zhao H, et al. Billion-scale commodity embedding for e-commerce recommendation in Alibaba. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2018. 839–848

    Google Scholar 

  24. Covington P, Adams J, Sargin E. Deep neural networks for YouTube recommendations. In: Proceedings of the 10th ACM Conference on Recommender Systems, 2016. 191–198

    Chapter  Google Scholar 

  25. Gomez-Uribe C A, Hunt N. The Netflix recommender system. ACM Trans Manage Inf Syst, 2015, 6: 1–19

    Article  Google Scholar 

  26. Liu J, Dolan P, Pedersen E R. Personalized news recommendation based on click behavior. In: Proceedings of the 15th International Conference on Intelligent User Interfaces, 2010. 31–40

    Chapter  Google Scholar 

  27. Wu F, Qiao Y, Chen J H, et al. Mind: a large-scale dataset for news recommendation. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020. 3597–3606

    Chapter  Google Scholar 

  28. Li Z, Zhao H, Liu Q, et al. Learning from history and present: next-item recommendation via discriminatively exploiting user behaviors. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2018. 1734–1743

    Google Scholar 

  29. Gu Y, Ding Z, Wang S, et al. Deep multifaceted transformers for multi-objective ranking in large-scale e-commerce recommender systems. In: Proceedings of the 29th ACM International Conference on Information and Knowledge Management, 2020. 2493–2500

    Google Scholar 

  30. Wang W, Zhang W, Liu S, et al. Beyond clicks: modeling multi-relational item graph for session-based target behavior prediction. In: Proceedings of the Web Conference 2020, 2020. 3056–3062

    Chapter  Google Scholar 

  31. Wu C, Wu F, Qi T, et al. Feedrec: news feed recommendation with various user feedbacks. In: Proceedings of the ACM Web Conference 2022, 2022. 2088–2097

    Chapter  Google Scholar 

  32. Zhou M, Ding Z, Tang J, et al. Micro behaviors: a new perspective in e-commerce recommender systems. In: Proceedings of the 11th ACM International Conference on Web Search and Data Mining, 2018. 727–735

    Google Scholar 

  33. Gu Y, Ding Z, Wang S, et al. Hierarchical user profiling for e-commerce recommender systems. In: Proceedings of the 13th International Conference on Web Search and Data Mining, 2020. 223–231

    Chapter  Google Scholar 

  34. Xu Y, Zhu Y, Yu J. Modeling multiple coexisting category-level intentions for next item recommendation. ACM Trans Inform Syst, 2021, 39: 1–24

    Google Scholar 

  35. Cho J, Hyun D, won Lim D, et al. Dynamic multi-behavior sequence modeling for next item recommendation. In: Proceedings of the AAAI Conference on Artificial Intelligence, 2023. 4199–4207

    Google Scholar 

  36. Su J, Chen C, Lin Z, et al. Personalized behavior-aware transformer for multi-behavior sequential recommendation. In: Proceedings of the 31st ACM International Conference on Multimedia, 2023. 6321–6331

    Chapter  Google Scholar 

  37. Zhan Z X, He M K, Pan W K, et al. TransRec++: translation-based sequential recommendation with heterogeneous feedback. Front Comput Sci, 2022, 16: 162615

    Article  Google Scholar 

  38. Chen W, He M, Ni Y, et al. Global and personalized graphs for heterogeneous sequential recommendation by learning behavior transitions and user intentions. In: Proceedings of the 16th ACM Conference on Recommender Systems, 2022. 268–277

    Chapter  Google Scholar 

  39. Luo J, He M, Pan W, et al. Bgnn: behavior-aware graph neural network for heterogeneous session-based recommendation. Front Comput Sci, 2023, 17: 175336

    Article  Google Scholar 

  40. Pan W, Yang K. Multi-behavior graph neural networks for session-based recommendation. In: Proceedings of the 3rd International Conference on Machine Learning, Big Data and Business Intelligence, 2021. 756–761

    Google Scholar 

  41. Luo J, He M, Lin X, et al. Dual-task learning for multi-behavior sequential recommendation. In: Proceedings of the 31st ACM International Conference on Information and Knowledge Management, 2022. 1379–1388

    Google Scholar 

  42. He M, Lin J, Luo J, et al. Flag: a feedback-aware local and global model for heterogeneous sequential recommendation. ACM Trans Intell Syst Tech, 2022, 14: 1–22

    Article  Google Scholar 

  43. Wu Y, Xie R, Zhu Y, et al. Multi-view multi-behavior contrastive learning in recommendation. In: Proceedings of the International Conference on Database Systems for Advanced Applications, 2022. 166–182

    Chapter  Google Scholar 

  44. Shen Y, Ou B, Li R. Mbn: towards multi-behavior sequence modeling for next basket recommendation. ACM Trans Knowl Discov Data, 2022, 16: 1–23

    Article  Google Scholar 

  45. Koren Y. Collaborative filtering with temporal dynamics. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2009. 447–456

    Chapter  Google Scholar 

  46. Liu Q, Zeng X, Liu C, et al. Mining indecisiveness in customer behaviors. In: Proceedings of the 2015 IEEE International Conference on Data Mining, 2015. 281–290

    Chapter  Google Scholar 

  47. Zhang F, Yuan N J, Lian D, et al. Collaborative knowledge base embedding for recommender systems. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016. 353–362

    Chapter  Google Scholar 

  48. Wu B, Mei T, Cheng W, et al. Unfolding temporal dynamics: predicting social media popularity using multi-scale temporal decomposition. In: Proceedings of the 30th AAAI Conference on Artificial Intelligence, 2016. 272–278

    Google Scholar 

  49. Yang Y, Huang C, Xia L, et al. Multi-behavior hypergraph-enhanced transformer for sequential recommendation. In: Proceedings of the 28th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2022. 2263–2274

    Google Scholar 

  50. Xia L, Huang C, Xu Y, et al. Multi-behavior sequential recommendation with temporal graph transformer. IEEE Trans Knowl Data Eng, 2022, 35: 6099–6112

    Google Scholar 

  51. Wei W, Xia L, Huang C. Multi-relational contrastive learning for recommendation. In: Proceedings of the 17th ACM Conference on Recommender Systems, 2023. 338–349

    Chapter  Google Scholar 

  52. Liu N N, Xiang E W, Zhao M, et al. Unifying explicit and implicit feedback for collaborative filtering. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, 2010. 1445–1448

    Chapter  Google Scholar 

  53. Hadash G, Shalom O S, Osadchy R. Rank and rate: multi-task learning for recommender systems. In: Proceedings of the 12th ACM Conference on Recommender Systems, 2018. 451–454

    Chapter  Google Scholar 

  54. Zhao X, Zhang L, Ding Z, et al. Recommendations with negative feedback via pairwise deep reinforcement learning. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2018. 1040–1048

    Google Scholar 

  55. He X, Zhang H, Kan M, et al. Fast matrix factorization for online recommendation with implicit feedback. In: Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2016. 549–558

    Chapter  Google Scholar 

  56. Xie R, Ling C, Wang Y, et al. Deep feedback network for recommendation. In: Proceedings of the 29th International Joint Conference on Artificial Intelligence, 2020. 2519–2525

    Google Scholar 

  57. Bian Z, Zhou S, Fu H, et al. Denoising user-aware memory network for recommendation. In: Proceedings of the 15th ACM Conference on Recommender Systems, 2021. 400–410

    Google Scholar 

  58. Johnson C C. Logistic matrix factorization for implicit feedback data. In: Proceedings of Advances in Neural Information Processing Systems, 2014

    Google Scholar 

  59. Mnih A, Salakhutdinov R R. Probabilistic matrix factorization. In: Proceedings of Advances in Neural Information Processing Systems, 2007

    Google Scholar 

  60. Desrosiers C, Karypis G. A comprehensive survey of neighborhood-based recommendation methods. In: Recommender Systems Handbook. Boston: Springer, 2011. 107–144

    Chapter  Google Scholar 

  61. Ma H, Yang H, Lyu M R, et al. Sorec: social recommendation using probabilistic matrix factorization. In: Proceedings of the 17th ACM Conference on Information and Knowledge Management, 2008. 931–940

    Chapter  Google Scholar 

  62. Mehta R, Rana K. A review on matrix factorization techniques in recommender systems. In: Proceedings of the 2nd International Conference on Communication Systems, Computing and IT Applications, 2017. 269–274

    Google Scholar 

  63. He R, Kang W, McAuley J. Translation-based recommendation. In: Proceedings of the 11th ACM Conference on Recommender Systems, 2017. 161–169

    Google Scholar 

  64. He R, McAuley J. Fusing similarity models with Markov chains for sparse sequential recommendation. In: Proceedings of the 16th IEEE International Conference on Data Mining, 2016. 191–200

    Google Scholar 

  65. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature, 2015, 521: 436–444

    Article  Google Scholar 

  66. Goodfellow I, Bengio Y, Courville A. Deep Learning. Cambridge: MIT Press, 2016

    Google Scholar 

  67. Collobert R, Weston J. A unified architecture for natural language processing: deep neural networks with multitask learning. In: Proceedings of the 25th International Conference on Machine Learning, 2008. 160–167

    Chapter  Google Scholar 

  68. Socher R, Perelygin A, Wu J, et al. Recursive deep models for semantic compositionality over a sentiment treebank. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, 2013. 1631–1642

    Chapter  Google Scholar 

  69. Dahl G E, Yu D, Deng L, et al. Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition. IEEE Trans Audio Speech Lang Process, 2011, 20: 30–42

    Article  Google Scholar 

  70. Krizhevsky A, Sutskever I, Hinton G E. Imagenet classification with deep convolutional neural networks. In: Proceedings of Advances in Neural Information Processing Systems, 2012

    Google Scholar 

  71. Le Q V. Building high-level features using large scale unsupervised learning. In: Proceedings of the 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2013. 8595–8598

    Google Scholar 

  72. Gao C, He X, Gan D, et al. Learning to recommend with multiple cascading behaviors. IEEE Trans Knowl Data Eng, 2019, 33: 2588–2601

    Article  Google Scholar 

  73. Zhou X, Liu D, Lian J, et al. Collaborative metric learning with memory network for multi-relational recommender systems. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence, 2019. 4454–4460

    Google Scholar 

  74. Wu C, Wu F, Cao Y, et al. Fedgnn: federated graph neural network for privacy-preserving recommendation. ArXiv:2102.04925

  75. Wu C, Wu F, Lyu L, et al. Fedctr: federated native ad ctr prediction with cross platform user behavior data. ACM Trans Intell Syst Tech, 2022, 13: 1–19

    Google Scholar 

  76. Mikolov T, Karafiát M, Burget L, et al. Recurrent neural network based language model. In: Proceedings of the Interspeech, 2010. 1045–1048

    Google Scholar 

  77. Bengio Y, Simard P, Frasconi P. Learning long-term dependencies with gradient descent is difficult. IEEE Trans Neural Netw, 1994, 5: 157–166

    Article  Google Scholar 

  78. Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput, 1997, 9: 1735–1780

    Article  Google Scholar 

  79. Chung J, Gulcehre C, Cho K, et al. Empirical evaluation of gated recurrent neural networks on sequence modeling. ArXiv:1412.3555

  80. Chen T, Yin H, Chen H, et al. Air: attentional intention-aware recommender systems. In: Proceedings of the 2019 IEEE 35th International Conference on Data Engineering, 2019. 304–315

    Google Scholar 

  81. Liu H, Ding J, Zhu Y, et al. Modeling multi-aspect preferences and intents for multi-behavioral sequential recommendation. Knowl-Based Syst, 2023, 280: 111013

    Article  Google Scholar 

  82. Le D, Lauw H W, Fang Y. Modeling contemporaneous basket sequences with twin networks for next-item recommendation. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence, 2018. 3414–3420

    Google Scholar 

  83. Guo L, Hua L, Jia R, et al. Buying or browsing?: predicting real-time purchasing intent using attention-based deep network with multiple behavior. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2019. 1984–1992

    Google Scholar 

  84. Han J, Ma Y, Mei Q, et al. Deeprec: on-device deep learning for privacy-preserving sequential recommendation in mobile commerce. In: Proceedings of The Web Conference 2021, 2021. 900–911

    Chapter  Google Scholar 

  85. Scarselli F, Gori M, Ah Chung Tsoi M, et al. The graph neural network model. IEEE Trans Neural Netw, 2008, 20: 61–80

    Article  Google Scholar 

  86. Wu Z, Pan S, Chen F, et al. A comprehensive survey on graph neural networks. IEEE Trans Neural Netw Learn Syst, 2020, 32: 4–24

    Article  MathSciNet  Google Scholar 

  87. Welling M, Kipf T N. Semi-supervised classification with graph convolutional networks. In: Proceedings of the 5th International Conference on Learning Representation, 2016

    Google Scholar 

  88. Hamilton W, Ying Z, Leskovec J. Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, 2017. 1025–1035

    Google Scholar 

  89. Velickovic P, Cucurull G, Casanova A, et al. Graph attention networks. In: Proceedings of the 6th International Conference on Learning Representation, 2018

    Google Scholar 

  90. Xiao F, Li L, Xu W, et al. DMBGN: deep multi-behavior graph networks for voucher redemption rate prediction. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2021. 3786–3794

    Chapter  Google Scholar 

  91. Yu B, Zhang R, Chen W, et al. Graph neural network based model for multi-behavior session-based recommendation. Geo Inform, 2022, 26: 429–447

    Google Scholar 

  92. Liang Y, Song Q, Zhao Z, et al. BA-GNN: behavior-aware graph neural network for session-based recommendation. Front Comput Sci, 2022, 6: 1–16

    Google Scholar 

  93. Li X, Chen H, Yu J, et al. Global heterogeneous graph and target interest denoising for multi-behavior sequential recommendation. In: Proceedings of the 17th ACM International Conference on Web Search and Data Mining, 2024. 387–395

    Chapter  Google Scholar 

  94. Li Y, Tarlow D, Brockschmidt M, et al. Gated graph sequence neural networks. In: Proceedings of the 4th International Conference on Learning Representations, 2016

    Google Scholar 

  95. Morris C, Ritzert M, Fey M, et al. Weisfeiler and Leman go neural: higher-order graph neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, 2019. 4602–4609

    Google Scholar 

  96. Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need. In: Proceedings of Advances in Neural Information Processing Systems, 2017

    Google Scholar 

  97. Devlin J, Chang M W, Lee K, et al. Bert: pre-training of deep bidirectional transformers for language understanding. ArXiv:1810.04805

  98. Liu Y, Ott M, Goyal N, et al. Roberta: a robustly optimized BERT pretraining approach. ArXiv:1907.11692

  99. Dosovitskiy A, Beyer L, Kolesnikov A, et al. An image is worth 16 × 16 words: transformers for image recognition at scale. ArXiv:2010.11929

  100. Carion N, Massa F, Synnaeve G, et al. End-to-end object detection with transformers. In: Proceedings of the European Conference on Computer Vision, 2020. 213–229

    Google Scholar 

  101. Tanjim M M, Su C, Benjamin E, et al. Attentive sequential models of latent intent for next item recommendation. In: Proceedings of The Web Conference 2020, 2020. 2528–2534

    Chapter  Google Scholar 

  102. Tran Q, Tran L, Hai L C, et al. From implicit to explicit feedback: a deep neural network for modeling sequential behaviours and long-short term preferences of online users. Neurocomputing, 2022, 479: 89–105

    Article  Google Scholar 

  103. He T, Li K, Chen S, et al. DMBIN: a dual multi-behavior interest network for click-through rate prediction via contrastive learning. In: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2023. 1366–1375

    Chapter  Google Scholar 

  104. Elsayed S, Rashed A, Schmidt-Thieme L. Multi-behavioral sequential recommendation. In: Proceedings of the 18th ACM Conference on Recommender Systems, 2024. 902–906

    Chapter  Google Scholar 

  105. Raffel C, Shazeer N, Roberts A, et al. Exploring the limits of transfer learning with a unified text-to-text transformer. J Mach Learn Res, 2020, 21: 1–67

    MathSciNet  Google Scholar 

  106. He M, Pan W, Ming Z. Bar: behavior-aware recommendation for sequential heterogeneous one-class collaborative filtering. Inform Sci, 2022, 608: 881–899

    Article  Google Scholar 

  107. Ma M, Na S, Wang H, et al. The graph-based behavior-aware recommendation for interactive news. Appl Intell, 2022, 52: 1913–1929

    Article  Google Scholar 

  108. Jia M, Liu F, Li X, et al. Hybrid graph neural network recommendation based on multi-behavior interaction and time sequence awareness. Electronics, 2023, 12: 1223

    Article  Google Scholar 

  109. Yu B, Li X, Fang J, et al. Memory-augmented meta-learning framework for session-based target behavior recommendation. World Wide Web, 2023, 26: 233–251

    Article  Google Scholar 

  110. Li Q, Ma H, Jin W, et al. Multi-interest network with simple diffusion for multi-behavior sequential recommendation. In: Proceedings of the 2024 SIAM International Conference on Data Mining, 2024. 734–742

    Chapter  Google Scholar 

  111. Xia L, Huang C, Xu Y, et al. Knowledge-enhanced hierarchical graph transformer network for multi-behavior recommendation. In: Proceedings of the 35th AAAI Conference on Artificial Intelligence, 2021. 4486–4493

    Google Scholar 

  112. Qiao J, Wang L. Modeling user micro-behaviors and original interest via Adaptive Multi-Attention Network for session-based recommendation. Knowl-Based Syst, 2022, 244: 108567

    Article  Google Scholar 

  113. Shao Z, Wang S, Lu W, et al. Filter-enhanced hypergraph transformer for multi-behavior sequential recommendation. In: Proceedings of the ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing, 2024. 6575–6579

    Google Scholar 

  114. Mukande T, Ali E, Caputo A, et al. A flash attention transformer for multi-behaviour recommendation. In: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, 2023. 4210–4214

    Chapter  Google Scholar 

  115. Wu S, Tang Y, Zhu Y, et al. Session-based recommendation with graph neural networks. In: Proceedings of AAAI Conference on Artificial Intelligence, 2019. 346–353

    Google Scholar 

  116. Ho J, Jain A, Abbeel P. Denoising diffusion probabilistic models. Adv Neural Inform Process Syst, 2020, 33: 6840–6851

    Google Scholar 

  117. Wang W, Xu Y, Feng F, et al. Diffusion recommender model. In: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2023. 832–841

    Chapter  Google Scholar 

  118. Li Z, Sun A, Li C. DiffuRec: a diffusion model for sequential recommendation. In: Proceedings of Advances in Neural Information Processing Systems, 2023, 1–28

    Google Scholar 

  119. Yang Z, Wu J, Wang Z, et al. Generate what you prefer: reshaping sequential recommendation via guided diffusion. In: Proceedings of Advances in Neural Information Processing Systems, 2024

    Google Scholar 

  120. Liu Q, Yan F, Zhao X, et al. Diffusion augmentation for sequential recommendation. In: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, 2023. 1576–1586

    Chapter  Google Scholar 

  121. Wu Z, Wang X, Chen H, et al. Diff4Rec: sequential recommendation with curriculum-scheduled diffusion augmentation. In: Proceedings of the 31st ACM International Conference on Multimedia, 2023. 9329–9335

    Chapter  Google Scholar 

  122. Wang S, Li B Z, Khabsa M, et al. Linformer: self-attention with linear complexity. ArXiv:2006.04768

  123. Sun F, Liu J, Wu J, et al. BERT4Rec: sequential recommendation with bidirectional encoder representations from transformer. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, 2019. 1441–1450

    Chapter  Google Scholar 

  124. He X, Deng K, Wang X, et al. LightGCN: simplifying and powering graph convolution network for recommendation. In: Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval, 2020. 639–648

    Chapter  Google Scholar 

  125. Dao T, Fu D, Ermon S, et al. Flashattention: fast and memory-efficient exact attention with IO-awareness. Adv Neural Inform Process Syst, 2022, 35: 16344–16359

    Google Scholar 

  126. Xie S, Liao Y, Cao K. Multi-behavior recommendation based on heterogeneous graph and attention networks. In: Proceedings of the 4th International Conference on Consumer Electronics and Computer Engineering, 2024. 200–203

    Google Scholar 

  127. Qu M, Wang N, Li J. A two-path multibehavior model of user interaction. Electronics, 2023, 12: 3048

    Article  Google Scholar 

  128. Li W, Wu Y, Liu Y, et al. BMLP: behavior-aware MLP for heterogeneous sequential recommendation. Front Comput Sci, 2024, 18: 183341

    Article  Google Scholar 

  129. Xing X, Zhang X, Cui J, et al. Multi-behavior attention mechanisms graph neural networks based on session recommendation. In: Proceedings of the 35th Chinese Control and Decision Conference, 2023. 4213–4217

    Google Scholar 

  130. Shi X, Shi Y, Wang J, et al. Category enhanced dual view contrastive learning for session-based recommendation. In: Proceedings of International Conference on Artificial Neural Networks, 2023. 285–297

    Google Scholar 

  131. Qin S, Lin F, Xu L, et al. Multi-behavior session-based recommendation via graph reinforcement learning. In: Proceedings of Asian Conference on Machine Learning, 2024. 1119–1134

    Google Scholar 

  132. Elsayed S, Rashed A, Schmidt-Thieme L. HMAR: hierarchical masked attention for multi-behaviour recommendation. In: Proceedings of Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2024. 131–143

    Chapter  Google Scholar 

  133. Gao Y, Huang X, Tao J. Sequential recommendation model integrating micro-behaviors and attribute enhancement. Neurocomputing, 2023, 548: 126393

    Article  Google Scholar 

  134. Chen Y, Cao Q, Huang X, et al. Multi-behavior collaborative contrastive learning for sequential recommendation. Complex Intell Syst, 2024, 10: 5033–5048

    Article  Google Scholar 

  135. Mukande T, Ali E, Caputo A, et al. A sparse sinkhorn transformer for multi-behaviour recommendation. In: Proceedings of the 2023 IEEE International Conference on e-Business Engineering, 2023. 90–96

    Google Scholar 

  136. Xu W, Li H, Wang M. Multi-behavior guided temporal graph attention network for recommendation. In: Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2023. 297–309

    Chapter  Google Scholar 

  137. Zhang C, Chen R, Zhao X, et al. Denoising and prompt-tuning for multi-behavior recommendation. In: Proceedings of the ACM Web Conference 2023, 2023. 1355–1363

    Chapter  Google Scholar 

  138. Wei C, Hu C, Wang C D, et al. Time-aware multibehavior contrastive learning for social recommendation. IEEE Trans Indust Inform, 2024, 20: 6424–6435

    Article  Google Scholar 

  139. Liang X, Zhu Y, Duan H, et al. MISR: a multiple behavior interactive enhanced learning model for social-aware recommendation. J Supercomput, 2023, 79: 14221–14244

    Article  Google Scholar 

  140. Zhao G, Wang X, Wang J, et al. Task recommendation for mobile crowd sensing system based on multi-view user dynamic behavior prediction. Peer-to-Peer Netw Appl, 2023, 16: 1536–1550

    Article  Google Scholar 

  141. Su T T, Xi W D, Xing X X, et al. Mcrec: multi-channel gated gifts recommendation. In: Proceedings of the 2023 IEEE International Conference on Data Mining, 2023. 548–557

    Google Scholar 

  142. Gu P, Hu H, Xu G. Modeling multi-behavior sequence via hypergru contrastive network for micro-video recommendation. Knowl-Based Syst, 2024, 298: 111841

    Article  Google Scholar 

  143. Pan Y, Gao C, Chang J, et al. Understanding and modeling passive-negative feedback for short-video sequential recommendation. In: Proceedings of the 17th ACM Conference on Recommender Systems, 2023. 540–550

    Chapter  Google Scholar 

  144. Liu Q, Zhou Z, Jiang G, et al. Deep task-specific bottom representation network for multi-task recommendation. In: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, 2023. 1637–1646

    Chapter  Google Scholar 

  145. Li J, Sun P, Wang Z, et al. Intent-aware ranking ensemble for personalized recommendation. In: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2023. 1004–1013

    Chapter  Google Scholar 

  146. Zhou X, Jin J, Ma L, et al. Script: sequential cross-meta-information recommendation in pretrain and prompt paradigm. In: Proceedings of the 2023 IEEE International Conference on Data Mining, 2023. 888–897

    Google Scholar 

  147. Krichene W, Rendle S. On sampled metrics for item recommendation. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2020. 1748–1757

    Chapter  Google Scholar 

  148. Dallmann A, Zoller D, Hotho A. A case study on sampling strategies for evaluating neural sequential item recommendation models. In: Proceedings of the 15th ACM Conference on Recommender Systems, 2021. 505–514

    Google Scholar 

  149. Guo G, Qiu H, Tan Z, et al. Resolving data sparsity by multi-type auxiliary implicit feedback for recommender systems. Knowl-Based Syst, 2017, 138: 202–207

    Article  Google Scholar 

  150. Li S, Jin X, Xuan Y, et al. Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting. In: Proceedings of Advances in Neural Information Processing Systems, 2019. 5243–5253

    Google Scholar 

  151. Hu J, Shen Y, Liu Y, et al. Hero-gang neural model for named entity recognition. ArXiv:2205.07177

  152. Lin X, Luo J, Pan J, et al. Multi-sequence attentive user representation learning for side-information integrated sequential recommendation. In: Proceedings of the 17th ACM International Conference on Web Search and Data Mining, 2024. 414–423

    Chapter  Google Scholar 

  153. Huang B, Luo J, Du W, et al. Cascaded cross attention for review-based sequential recommendation. In: Proceedings of the 2023 IEEE International Conference on Data Mining, 2023. 170–179

    Google Scholar 

  154. Wang Q, Ji Z, Liu H, et al. Deep Bayesian multi-target learning for recommender systems. ArXiv:1902.09154

  155. Ma J, Zhao Z, Yi X, et al. Modeling task relationships in multi-task learning with multi-gate mixture-of-experts. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2018. 1930–1939

    Google Scholar 

  156. Guo L, Tang L, Chen T, et al. Da-gcn: a domain-aware attentive graph convolution network for shared-account cross-domain sequential recommendation. In: Proceedings of IJCAI International Joint Conference on Artificial Intelligence, 2021. 2483–2489

    Google Scholar 

  157. Li P, Jiang Z, Que M, et al. Dual attentive sequential learning for cross-domain click-through rate prediction. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, 2021. 3172–3180

    Chapter  Google Scholar 

  158. Ma M, Ren P, Chen Z, et al. Mixed information flow for cross-domain sequential recommendations. ACM Trans Knowl Discov Data, 2022, 16: 64

    Article  Google Scholar 

  159. Floridi L, Chiriatti M. GPT-3: its nature, scope, limits, and consequences. Minds Mach, 2020, 30: 681–694

    Article  Google Scholar 

  160. Zhao W X, Zhou K, Li J, et al. A survey of large language models. ArXiv:2303.18223

  161. Wu L, Zheng Z, Qiu Z, et al. A survey on large language models for recommendation. ArXiv:2305.19860

  162. Zhao Z, Fan W, Li J, et al. Recommender systems in the era of large language models (LLMs). IEEE Trans Knowl Data Eng, 2024, 36: 6889–6907

    Article  Google Scholar 

  163. Wang S, Zhangb X, Wang Y, et al. Trustworthy recommender systems. ArXiv:2208.06265

  164. McMahan B, Moore E, Ramage D, et al. Communication-efficient learning of deep networks from decentralized data. In: Proceedings of Artificial Intelligence and Statistics, 2017. 1273–1282

    Google Scholar 

  165. Yang Q, Liu Y, Chen T, et al. Federated machine learning: concept and applications. ACM Trans Intell Syst Tech, 2019, 10: 1–19

    Google Scholar 

Download references

Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant Nos. 62461160311, 62172283, 62272315), Guangdong Basic and Applied Basic Research Foundation (Grant No. 2024A1515010122), and National Key Research and Development Program of China (Grant No. 2023YFF0725100). We thank Mr. Jinwei LUO for his helpful discussions.

Author information

Authors and Affiliations

  1. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China

    Xiaoqing Chen, Zhitao Li & Weike Pan

  2. Academy for Artificial Intelligence, Hong Kong Polytechnic University, Hong Kong, 999077, China

    Qiang Yang

  3. Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen, 518123, China

    Zhong Ming

  4. College of Big Data and Internet, Shenzhen Technology University, Shenzhen, 518118, China

    Zhong Ming

Authors
  1. Xiaoqing Chen
    View author publications

    Search author on:PubMed Google Scholar

  2. Zhitao Li
    View author publications

    Search author on:PubMed Google Scholar

  3. Weike Pan
    View author publications

    Search author on:PubMed Google Scholar

  4. Qiang Yang
    View author publications

    Search author on:PubMed Google Scholar

  5. Zhong Ming
    View author publications

    Search author on:PubMed Google Scholar

Corresponding authors

Correspondence to Weike Pan or Zhong Ming.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, X., Li, Z., Pan, W. et al. A survey on multi-behavior sequential recommendation. Sci. China Inf. Sci. 69, 131101 (2026). https://doi.org/10.1007/s11432-024-4568-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Version of record:

  • DOI: https://doi.org/10.1007/s11432-024-4568-7

Keywords