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App. Res. of Comp. Accepted Paper
Vol. 42, 2025 No. 8

Multi behavior recommendation based on auxiliary behavior denoising

Chen Wenhao
Chen Yuan
Zhu Xiaofei
Dept. of Computer Science & Technology, Chongqing 400054, China
DOI: 10.19734/j.issn.1001-3695.2025.01.0004

Abstract

Multi-behavior recommendation (MBR) systems analyze user preferences through auxiliary behaviors to alleviate data sparsity and enhance recommendation accuracy. However, previous approaches that used randomly initialized user and item embeddings failed to provide sufficient information value and ignored the noise in behavior embedding aggregation as well as the noise in auxiliary behavior interaction sequences. To overcome these limitations, this work introduces the Auxiliary Behavior Denoising MBR model (ABD-MBR) . The framework first pre-training users and items to generate meaningful initial embeddings. A projection aggregation module then maps auxiliary behaviors to the target behavior space, minimizing noise interference during embedding fusion. Additionally, the model implements bot-k and top-k sampling modules to refine auxiliary behavior interaction sequences, effectively suppressing sequence-level noise. Finally, the model employs multi-task learning to optimize its performance. Experiments on three publicly available datasets show that, compared to MB-HGCN, the model achieves an average improvement of 10.2% in HR@10 and 13.4% in NDCG@10, demonstrating its effectiveness.

Foundation Support

国家自然科学基金资助项目(62472059)
重庆市自然科学基金面上项目(CSTB2022NSCQ-MSX1672)
重庆英才计划项目(CSTC2024YCJH-BGZXM0022)
重庆市教育委员会科学技术研究计划重大项目(KJZD-M202201102)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.01.0004
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 8

Publish History

[2025-03-25] Accepted Paper

Cite This Article

陈文浩, 陈媛, 朱小飞. 基于辅助行为去噪的多行为推荐 [J]. 计算机应用研究, 2025, 42 (8). (2025-04-17). https://doi.org/10.19734/j.issn.1001-3695.2025.01.0004. (Chen Wenhao, Chen Yuan, Zhu Xiaofei. Multi behavior recommendation based on auxiliary behavior denoising [J]. Application Research of Computers, 2025, 42 (8). (2025-04-17). https://doi.org/10.19734/j.issn.1001-3695.2025.01.0004. )

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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