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

Cross-domain recommendation method based on aggregation of intra-domain and inter-domain meta-paths

Xu Jia1,2
Wang Xin3
Wang Yanqiu4
Wu Haiwei3
Lyu Pin1
1. Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou Guangdong 510006, China
2. State Key Laboratory of Cyberspace Security Defense, Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100085, China
3. School of Computer, Electronic & Information, Guangxi University, Nanning Guangxi 530004, China
4. School of Artificial Intelligence, Shenzhen Polytechnic University, Shenzhen Guangdong 518055, China
DOI: 10.19734/j.issn.1001-3695.2025.01.0022

Abstract

Cross-domain recommendation technology effectively enhances the recommendation performance of the target domain by deeply mining and utilizing useful information from other domains, providing an effective solution to user cold-start problem. However, existing cross-domain recommendation methods have limitations, failing to finely expand implicit relationships and neglecting potential redundant information in embedding vectors, which restricts the performance of cross-domain recommendation systems. To address this, the Intra-domain and inter-domain Meta-paths aggregation based Cross-Domain Recommendation method (IMCDR) is introduced. Specifically, IMCDR first calculates the fine-grained semantic embedding of entities across multiple fields, thereby effectively extending user-user and item-item relations. Then, IMCDR generates private features and shared features for each node based on intra-domain meta-paths and inter-domain meta-paths respectively, and effectively integrates them to obtain better quality embedding vectors. Experimental results across three cross-domain recommendation tasks demonstrate that IMCDR significantly outperforms in terms of precision and overall performance.

Foundation Support

国家自然科学基金资助项目(62067001)
深圳职业技术大学校级重点科研项目(6025310013K)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.01.0022
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 8

Publish History

[2025-04-16] Accepted Paper

Cite This Article

许嘉, 王歆, 王艳秋, 等. 基于域内和域间元路径聚合的跨域推荐方法 [J]. 计算机应用研究, 2025, 42 (8). (2025-04-17). https://doi.org/10.19734/j.issn.1001-3695.2025.01.0022. (Xu Jia, Wang Xin, Wang Yanqiu, et al. Cross-domain recommendation method based on aggregation of intra-domain and inter-domain meta-paths [J]. Application Research of Computers, 2025, 42 (8). (2025-04-17). https://doi.org/10.19734/j.issn.1001-3695.2025.01.0022. )

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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