Sequential Recommendation System Based on Deep Learning: A Survey

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.

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Publication Details

Journal
Electronics
Published
2025-05-24
DOI
https://doi.org/10.3390/electronics14112134
Citations
7
Primary Topic
Recommender Systems and Techniques
Type
article
Field-Weighted Citation Impact
22.72

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Sequential Recommendation System Based on Deep Learning: A Survey

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7 citations
Electronics
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22.72
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Sequential Recommendation System Based on Deep Learning: A Survey

Yi Liu, Wenying Sun, Peiyang Wei, Zhenzhen Hu, Tinghui Chen, Xun Deng, Hongping Shu, Can Hu, Yonghong Deng, Wen Qin, Jianhong Gan, Zhibin Li
article en
7 citations

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.

ElectronicsVol. 14(11)
Chongqing University of Posts and Telecommunications (CN), Southern Methodist University (US), Chinese Academy of Sciences (CN), Chengdu University of Information Technology (CN), Sichuan University of Arts and Science (CN), Xinjiang Technical Institute of Physics & Chemistry (CN), Chongqing Institute of Green and Intelligent Technology (CN), State Key Laboratory of Remote Sensing Science (CN), Sichuan Normal University (CN)
Chengdu University of Information Technology, Sichuan University, Chengdu University
Openalex Percentile: Top 1%
Recommender Systems and Techniques
22.72
DOIOpen Access PDFOpenAlex
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