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Algorithm Research & Explore
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2333-2339

Cross-domain recommendation under graph generation process

Cai Ruichu
Wu Fengzhu
Li Zijian
School of Computer Science, Guangdong University of Technology, Guangzhou 510006, China

Abstract

Recommendation systems are widely used everywhere and have a great influence on daily life. Aiming to train an ideal recommendation system, a massive of use-item interactive pairs should be provided. However, the dataset obtained is usually sparse, which might result in an over-fitting model and be hard to obtain the satisfying performance. In order to address this problem, the cross-domain recommendation is raised. Most of the existing methods on cross-domain recommendation systems borrow the ideas of the conventional unsupervised domain adaptation, which employ the feature alignment or adversarial training methods. Hence they can transfer the domain-invariant interests of users from the source to the target domains, e. g., from the Douban Movies to the Douban Books. However, since the network structures vary with different recommendation platforms, the existing methods on cross-domain recommendation systems straight forwardly extract the domain-invariant representation may entangle the structure information, which may result in the false alignment phenomenon. Furthermore, the previous efforts ignore the noise information behind the graph data, which further degenerate the experimental performance. In order to address the aforementioned problems, this paper brought the causal generation process of graph data into the cross-domain recommendation systems, it used the semantic latent variables of each node to calculate the relationships between users and items via disentangling the semantic latent variables, domain latent variables and noise latent variables. Experiments show that the proposed method yields state-of-the-art performance on several cross-domain recommendation system benchmark datasets.

Foundation Support

国家自然科学基金资助项目(61876043)
国家优秀青年科学基金资助项目(6212200101)
广州市科技计划资助项目(201902010058)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.01.0015
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 8
Section: Algorithm Research & Explore
Pages: 2333-2339
Serial Number: 1001-3695(2022)08-016-2333-07

Publish History

[2022-03-18] Accepted Paper
[2022-08-05] Printed Article

Cite This Article

蔡瑞初, 吴逢竹, 李梓健. 基于图生成过程的跨领域推荐 [J]. 计算机应用研究, 2022, 39 (8): 2333-2339. (Cai Ruichu, Wu Fengzhu, Li Zijian. Cross-domain recommendation under graph generation process [J]. Application Research of Computers, 2022, 39 (8): 2333-2339. )

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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