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App. Res. of Comp. Accepted Paper
Vol. 41, 2024 No. 12

Social knowledge recommendation based on graph reconstruction

Zhang Xinyue
Gao Hui
School of Computer Science & Engineering, University of Electronic Science & Technology of China, Cheng du 611731, China
DOI: 10.19734/j.issn.1001-3695.2024.05.0145

Abstract

Most existing recommendation models focus on explicitly constructing the relationship between users and items, neglecting to model the high-order global features of graph structure and under-mining the implicit interests of users. Therefore, we propose a social knowledge recommendation based on graph reconstruction (SKRGR) , which introduces graph reconstruction technology to decompose the user-item knowledge graph into multiple subgraphs, and utilizes relationship-based graph attention networks and three-layer graph neural networks to encode them independently. By implementing the neighborhood enhancement strategy, we deeply explore users' implicit interests and facilitate the modeling of neighborhood local features. To further improve the quality of the node representations, we introduce a global contrastive learning mechanism to unify the node representations of the global interaction graph and the local collaboration graph, and use a gate fusion strategy to control the aggregation of global and local information. Experimental results on the Ciao and Epinions social datasets indicate that the SKRGR model achieves an average improvement of 8.77% in Recall and 13.40% in NDCG. Compared to baseline models such as DSL and CLDS, SKRGR demonstrates significant performance advantages, validating its effectiveness in capturing users' implicit interests and modeling global features.

Foundation Support

四川省科技计划(2023YFG0021)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.05.0145
Publish at: Application Research of Computers Accepted Paper, Vol. 41, 2024 No. 12

Publish History

[2024-09-06] Accepted Paper

Cite This Article

张馨月, 高辉. 基于图重构的社交知识推荐 [J]. 计算机应用研究, 2024, 41 (12). (2024-09-11). https://doi.org/10.19734/j.issn.1001-3695.2024.05.0145. (Zhang Xinyue, Gao Hui. Social knowledge recommendation based on graph reconstruction [J]. Application Research of Computers, 2024, 41 (12). (2024-09-11). https://doi.org/10.19734/j.issn.1001-3695.2024.05.0145. )

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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