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App. Res. of Comp. Accepted Paper
Vol. 42, 2025 No. 5

Debiased causal recommendation based on graph neural networks

Xun Yaling
Li Xinyi
Han Shuo
Li Yanfeng
Wang Xing
College of Computer Science & Technology, Taiyuan University of Science & Technology, Taiyuan 030024, China
DOI: 10.19734/j.issn.1001-3695.2024.10.0362

Abstract

Recommendation systems typically rely on users' historical interaction data for model training. Although they can better reflect users' past behavioral preferences, they are inadequate in capturing users' potential interests and also face the issue of data sparsity. Additionally, recommendation systems tend to overemphasize popular items, failing to sufficiently consider users' genuine preferences, thereby limiting the diversity and personalization of recommendations. To address this issue, this paper proposed a debiased causal recommendation method called GDCR. First, GDCR introduced GNNs to aggregate information from both user-item interaction graphs and social network graphs. This process not only considered the differences in user ratings for different items, but also conducted in-depth analysis based on the closeness of relationships between users, in order to obtain richer and more comprehensive user and item representations. Then, GDCR constructed a causal graph to describe the process of data generation. The causal graph analysis reveals that in addition to popularity bias, over-recommendation of popular items is also influenced by consistency bias. Therefore, GDCR applied a backdoor adjustment strategy to eliminate these biases. Compared with eight baseline methods on the MovieLens and Douban-Movie public datasets, the results show that the proposed GDCR method achieves significant performance improvements over other SOTA recommendation approaches, further validating its effectiveness in addressing data sparsity issues and enhancing recommendation accuracy.

Foundation Support

国家自然科学基金资助项目(62272336)
山西省自然科学基金资助项目(202103021224286)
山省重点实验室项目(202204010931026)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.10.0362
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 5

Publish History

[2025-01-17] Accepted Paper

Cite This Article

荀亚玲, 李欣意, 韩硕, 等. 基于图神经网络的去偏因果推荐 [J]. 计算机应用研究, 2025, 42 (5). (2025-03-06). https://doi.org/10.19734/j.issn.1001-3695.2024.10.0362. (Xun Yaling, Li Xinyi, Han Shuo, et al. Debiased causal recommendation based on graph neural networks [J]. Application Research of Computers, 2025, 42 (5). (2025-03-06). https://doi.org/10.19734/j.issn.1001-3695.2024.10.0362. )

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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