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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 42, 2025 No. 2
391-397

Multi-behavior recommendation integrating self-attention and contrastive learning

Zhang Zhiwei
Sun Fuzhen
Sun Xiujuan
Li Pengcheng
Wang Shaoqing
School of Computer Science & Technology, Shandong University of Technology, Zibo Shandong 255049, China
DOI: 10.19734/j.issn.1001-3695.2024.07.0289

Abstract

Existing multi-behavior recommendation models ignore the optimization imbalance problem that exists between different behaviors. To solve this problem, this paper proposed a multi-behavior recommendation model integrating self-attention and contrastive learning(SACL). Firstly, it constructed independent interaction views based on different types of user-item interaction behaviors, and explored correlation relationships between users and items through graph neural networks to extract different behavior characteristics and interest preference features of users. Secondly, it performed contrastive learning between behaviors and users to capture the same user characteristics under different behaviors and enhanced the utilization of auxiliary behavior information. Then, it designed a multi-behavior optimization module based on the self-attention mechanism which defined different encoding methods based on the multi-behavior features and contrastive learning features of users to generate meta-knowledge with behavior dependencies. It designed a self-attentive multi-behavior loss weighting network to balance the training loss weights of different behaviors based on meta-knowledge, thus distinguishing the differences in the impacts on target behavior and reducing the auxiliary behavior noises. Experiments on the Tmall dataset and the IJCAI-Contest dataset show that the proposed model improves the hit rate(HR) of SACL by an average of 10% and the normalized discount rate(NDCG) by an average of 14% compared to the optimal baseline DPT, which verifies the effectiveness of SACL model for balanced optimization of multi-behavior recommendation tasks.

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.1001-3695.2024.07.0289
Publish at: Application Research of Computers Printed Article, Vol. 42, 2025 No. 2
Section: Algorithm Research & Explore
Pages: 391-397
Serial Number: 1001-3695(2025)02-009-0391-07

Publish History

[2025-02-05] Printed Article

Cite This Article

张志伟, 孙福振, 孙秀娟, 等. 融入自注意力和对比学习的多行为推荐 [J]. 计算机应用研究, 2025, 42 (2): 391-397. (Zhang Zhiwei, Sun Fuzhen, Sun Xiujuan, et al. Multi-behavior recommendation integrating self-attention and contrastive learning [J]. Application Research of Computers, 2025, 42 (2): 391-397. )

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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