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Electronics
Volume 14
Issue 11
10.3390/electronics14112134
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Review

Sequential Recommendation System Based on Deep Learning: A Survey

by
Peiyang Wei
1,2,3,4,5,*,
Hongping Shu
2,4,
Jianhong Gan
2,4,6,
Xun Deng
7,
Yi Liu
2,
Wenying Sun
8,
Tinghui Chen
1,
Can Hu
2,
Zhenzhen Hu
2,6,
Yonghong Deng
2,6,
Wen Qin
6,9 and
Zhibin Li
2,6,7
1
School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2
School of Software Engineering, Chengdu University of Information Technology, Chengdu 610225, China
3
Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China
4
Automatic Software Generation & Intelligence Service Key Laboratory of Sichuan Province, Chengdu 610225, China
5
Key Laboratory of Remote Sensing Application and Innovation, Chongqing 401147, China
6
Dazhou Key Laboratory of Government Data Security, Sichuan University of Arts and Science, Dazhou 635000, China
7
Xinjiang Technical Institute of Physics &Chemistry, Chinese Academy of Sciences, Urumqi 830011, China
8
Lyle School of Engineering, Southern Methodist University, Dallas, TX 75205, USA
9
School of Computer Science, Sichuan Normal University, Chengdu 610101, China
*
Author to whom correspondence should be addressed.
Electronics 2025, 14(11), 2134; https://doi.org/10.3390/electronics14112134 (registering DOI)
Submission received: 5 April 2025 / Revised: 18 May 2025 / Accepted: 21 May 2025 / Published: 24 May 2025
(This article belongs to the Section Computer Science & Engineering)
Versions Notes

Abstract

With the rapid development of deep learning in artificial intelligence, sequential recommendation systems play an increasingly important role in e-commerce, social media, digital entertainment, and other fields. This work systematically reviews the research progress of deep learning in sequential recommendation systems from a methodological perspective. This paper focuses on analyzing three dominant technical paradigms: contrastive learning, graph neural networks, and attention mechanisms, elucidating their theoretical innovations and evolutionary trajectories in sequential recommendation systems. Through empirical investigation, we categorize the prevailing evaluation metrics, benchmark datasets, and characteristic distributions of typical application scenarios within this domain. This work further proposes promising avenues for sequential recommendation systems in the future.
Keywords: sequential recommendation system; deep learning; graph neural network; contrastive learning; attention mechanism sequential recommendation system; deep learning; graph neural network; contrastive learning; attention mechanism

Share and Cite

MDPI and ACS Style

Wei, P.; Shu, H.; Gan, J.; Deng, X.; Liu, Y.; Sun, W.; Chen, T.; Hu, C.; Hu, Z.; Deng, Y.; et al. Sequential Recommendation System Based on Deep Learning: A Survey. Electronics 2025, 14, 2134. https://doi.org/10.3390/electronics14112134

AMA Style

Wei P, Shu H, Gan J, Deng X, Liu Y, Sun W, Chen T, Hu C, Hu Z, Deng Y, et al. Sequential Recommendation System Based on Deep Learning: A Survey. Electronics. 2025; 14(11):2134. https://doi.org/10.3390/electronics14112134

Chicago/Turabian Style

Wei, Peiyang, Hongping Shu, Jianhong Gan, Xun Deng, Yi Liu, Wenying Sun, Tinghui Chen, Can Hu, Zhenzhen Hu, Yonghong Deng, and et al. 2025. "Sequential Recommendation System Based on Deep Learning: A Survey" Electronics 14, no. 11: 2134. https://doi.org/10.3390/electronics14112134

APA Style

Wei, P., Shu, H., Gan, J., Deng, X., Liu, Y., Sun, W., Chen, T., Hu, C., Hu, Z., Deng, Y., Qin, W., & Li, Z. (2025). Sequential Recommendation System Based on Deep Learning: A Survey. Electronics, 14(11), 2134. https://doi.org/10.3390/electronics14112134

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