App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 4

End-to-end learning framework for multi-level diversified recommendation

Wen Wen1
Guo Xiaotong1
Feng Yali1
Zheng Jiabi2,3
Hao Zhifeng4
1. School of Computer Science, Guangdong University of Technology, Guangzhou 510006, China
2. College of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, China
3. State Key Laboratory for Novel Software Technology at Nanjing University, Nanjing 210023, China
4. Shantou University, Shantou Guangdong 515000, China
DOI: 10.19734/j.issn.1001-3695.2025.08.0280

Abstract

Diversified recommendation aims to alleviate the information cocoon effect and improve user satisfaction by increasing the diversity among items in the recommendation list and reducing low-quality repetitive recommendations. However, existing diversified recommendation methods routinely disregard the personalized diversity requirements when promoting recommendation list diversity. Moreover, most existing methods still rely on staged learning or staged optimization strategy, which limit the improvement of recommendation performance. Therefore, proposing an end-to-end learning framework for multi-level diversified recommendation (MLDR) from the perspective of personalized requirements. By introducing constraints that capture users' multi-level diversified requirements into collaborative filtering models such as matrix factorization or graph neural networks, and designing objective functions that balance both recommendation accuracy and diversity, the reasonable capture of user preferences is achieved. Extensive experiments on four datasets demonstrate that MLDR performs significantly better than the state-of-the-art baselines, and it improves the diversity of base models while preserving recommendation accuracy.

Foundation Support

广东省自然科学基金资助项目(2024A1515011380)
计算机软件新技术国家重点实验室开放课题(KFKT2022B16)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.08.0280
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 4

Publish History

[2025-12-11] Accepted Paper

Cite This Article

温雯, 郭晓彤, 冯雅莉, 等. 面向用户多层次多样化推荐的端到端学习框架 [J]. 计算机应用研究, 2026, 43 (4). (2025-12-19). https://doi.org/10.19734/j.issn.1001-3695.2025.08.0280. (Wen Wen, Guo Xiaotong, Feng Yali, et al. End-to-end learning framework for multi-level diversified recommendation [J]. Application Research of Computers, 2026, 43 (4). (2025-12-19). https://doi.org/10.19734/j.issn.1001-3695.2025.08.0280. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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