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App. Res. of Comp. Accepted Paper
Vol. 42, 2025 No. 9

Side-information integrated sequential recommendation model based on multi-sequence interaction and contrastive learning

Zhao Wei
Sun Fuzhen
Zhang Wenxuan
Wang Aofei
Wang Shaoqing
School of Computer Science & Technology, Shandong University of Technology, Zibo Shandong 255000, China
DOI: 10.19734/j.issn.1001-3695.2025.02.0025

Abstract

Existing side-information integrated sequence recommendation models suffer from insufficient user representation learning and optimization. To solve this problem, this paper proposed a Side-Information Integrated Sequential Recommendation Model Based on Multi-Sequence Interaction and Contrastive Learning (MICL) . Firstly, it introduced a multi-sequence interaction attention mechanism to construct deep intra-sequence and inter-sequence associations for item sequences and side-information sequences. This mechanism captured user preferences from both item and side-information perspectives and generated user representations from two viewpoints. Secondly, this method used a user representation optimization module and a dynamic hard negative sampling strategy to construct positive and negative sample pairs. It employed self-supervised signals to optimize user representations. Finally, it adopted a multi-task dynamic weight adjustment strategy to achieve a dynamic balance between recommendation and attribute prediction tasks, thus enhancing the model’s robustness and generalization ability. The model was tested on four public datasets: Beauty, Sports, Toys, and Yelp. Compared to well-performing baseline models, the proposed model improves the recall rate (Recall) of MICL and the normalized discount rate (NDCG) of MICL by 1.63% and 2.35% on average. Experimental results verify the effectiveness of MICL in learning and optimizing user representations.

Foundation Support

国家自然科学基金资助项目(61841602)
山东省自然科学基金资助项目(ZR2020MF147)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.02.0025
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 9

Publish History

[2025-05-09] Accepted Paper

Cite This Article

赵伟, 孙福振, 张文轩, 等. 基于多序列交互与对比学习的侧信息集成序列推荐模型 [J]. 计算机应用研究, 2025, 42 (9). (2025-05-27). https://doi.org/10.19734/j.issn.1001-3695.2025.02.0025. (Zhao Wei, Sun Fuzhen, Zhang Wenxuan, et al. Side-information integrated sequential recommendation model based on multi-sequence interaction and contrastive learning [J]. Application Research of Computers, 2025, 42 (9). (2025-05-27). https://doi.org/10.19734/j.issn.1001-3695.2025.02.0025. )

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.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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