Special Topic on Natural Language Processing
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 1
65-71

Improving social recommendation algorithm via incorporating interaction strength

Zhou Luxin
Li Man
Jiang Mingyang
Zhang Lei
School of Mathematics & Statistics, Chongqing Jiaotong University, Chongqing 400074, China
DOI: 10.19734/j.issn.1001-3695.2023.05.0280

Abstract

Existing social recommendation algorithms ignore the investigation on the association between rating information and social information. To address this issue, this paper proposed a social recommendation algorithm which incorporated interaction strength. Firstly, it utilized social information and rating data to enrich the social matrix by combining two kinds of similarities. Secondly, it defined the interaction strength to represent complex relationship between users. Finally, it introduced a new objective function to learn features of users and items for personalized recommendation using two types of associations, namely the association between interaction strength and social relationships, and the association between features of users and participation features of group which users belonged to. Experimental results on three real-world datasets indicate that the proposed algorithm shows significant improvement in terms of recommendation prediction accuracy compared with existing baseline mo-dels. Furthermore, the proposed algorithm behaves good robustness in learning latent features for users with different number of ratings. Based on the above observations, it can infer that incorporating interaction strength is beneficial to enhancing social recommendation performance and improving users' experience.

Foundation Support

国家自然科学基金资助项目(62276034)
重庆市教育委员会科学技术研究项目(KJQN202100712)
重庆市研究生科研创新项目(CYS22429)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.05.0280
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 1
Section: Special Topic on Natural Language Processing
Pages: 65-71
Serial Number: 1001-3695(2024)01-010-0065-07

Publish History

[2023-10-07] Accepted Paper
[2024-01-05] Printed Article

Cite This Article

周璐鑫, 李曼, 蒋明阳, 等. 融合交互强度的优化社交推荐算法 [J]. 计算机应用研究, 2024, 41 (1): 65-71. (Zhou Luxin, Li Man, Jiang Mingyang, et al. Improving social recommendation algorithm via incorporating interaction strength [J]. Application Research of Computers, 2024, 41 (1): 65-71. )

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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