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App. Res. of Comp. Accepted Paper
Vol. 42, 2025 No. 11

Recommendation method based on rating prediction and graph model diffusion

Wang Liu1,2,3
Chen Xuebin1,2,3
Gao Yuan1,2,3
Ma Kaiguang1,2,3
Zhao Tong1,2,3
1. College of Science, North China University of Science & Technology, Tangshan Hebei 063210, China
2. Hebei Key Laboratory of Data Science & Application, Tangshan Hebei 063210, China
3. Tangshan Key Laboratory of Data Science, Tangshan Hebei 063210, China
DOI: 10.19734/j.issn.1001-3695.2025.03.0095

Abstract

To address the issues of data sparsity and limited recommendation scope in collaborative filtering algorithms, a recommendation method based on rating prediction and graph model diffusion, named SIRR (Slope One and Item-based Collaborative Filtering with Random Walk Recommendation) , was proposed. Firstly, a dynamic switching mechanism was designed based on the number of user ratings to predict ratings for unrated items, aiming to address the data sparsity problem. Secondly, the accuracy of similarity computation and the robustness of the collaborative filtering algorithm were improved using regularized cosine similarity. Finally, to overcome the limitation of localized recommendations, a weighted random walk on the graph was applied to expand the recommendation scope, enhancing coverage. To balance recommendation accuracy and diversity, an optimization was achieved by integrating rating weights. The effectiveness of regularized cosine similarity was validated on two datasets of different types. The proposed method was compared with three baseline algorithms on three datasets with varying sparsity levels. Simulation results showed that SIRR performed well across all evaluation metrics. It provides an effective solution to data sparsity and local recommendation problems.

Foundation Support

国家自然科学基金资助项目(U20A20179)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.03.0095
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 11

Publish History

[2025-07-03] Accepted Paper

Cite This Article

王柳, 陈学斌, 高远, 等. 基于评分预测与图模型扩散的推荐方法 [J]. 计算机应用研究, 2025, 42 (11). (2025-07-08). https://doi.org/10.19734/j.issn.1001-3695.2025.03.0095. (Wang Liu, Chen Xuebin, Gao Yuan, et al. Recommendation method based on rating prediction and graph model diffusion [J]. Application Research of Computers, 2025, 42 (11). (2025-07-08). https://doi.org/10.19734/j.issn.1001-3695.2025.03.0095. )

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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