App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 12

Harnessing reciprocal influence of social connections and user interactions for recommendation

Xie Chang1
Yang Mingming1,2
Cao Wenming1,2
Li Man1,2
Guan Lihe1,2
Zheng Wei3
1. School of Mathematics and Statistics, Chongqing Jiaotong University, Chongqing 400074, China
2. Key Laboratory of Complex Systems Optimization and Intelligent Control of Chongqing Municipal Education Commission, Chongqing 400074, China
3. School of Computer and Information Technology, Shanxi University, Taiyuan Shanxi 030006, China
DOI: 10.19734/j.issn.1001-3695.2026.04.0111

Abstract

Existing social recommendation system methods still suffer from two critical limitations: social embeddings provide insufficient guidance for user preferences, while preference embeddings lack explicit structural consistency constraints. These shortcomings hinder the effective integration of social relationships and user interaction behaviors, which severely limits recommendation performance. To address these issues, we propose HUIR, a new social recommendation scheme which exploits the reciprocal influence between social connections and user interactions. Specifically, we first design an interaction-driven social embedding refinement module to align social graph embeddings with interaction-based preference representations for enriching the social embeddings with preference-aware information. Next, we propose a social-relation-guided preference consistency module that incorporates social structural knowledge and leverages contrastive learning to enforce structural consistency in user preference representations. Experimental results over three benchmark datasets demonstrate the superiority of HUIR over state-of-the-art methods. Compared with the strongest baseline, HUIR achieves average improvements of 11.36% and 11.17% in terms of Recall@10 and Recall@20, respectively, as well as 9.62% and 8.52% gains in NDCG@10 and NDCG@20. The source code is publicly available at: https: //github. com/cqjtumathai/HUIR.

Foundation Support

国家自然科学基金委员会资助项目(12401678,62306052,12401672)
重庆市教育委员会科学技术研究计划资助项目(KJQN202400716,KJQN202300735,KJQN202500758)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2026.04.0111
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 12

Publish History

[2026-08-04] Accepted Paper

Cite This Article

解畅, 杨明明, 曹文明, 等. 社交关系与用户交互双向驱动的推荐模型 [J]. 计算机应用研究, 2026, 43 (12). (2026-08-25). https://doi.org/10.19734/j.issn.1001-3695.2026.04.0111. (Xie Chang, Yang Mingming, Cao Wenming, et al. Harnessing reciprocal influence of social connections and user interactions for recommendation [J]. Application Research of Computers, 2026, 43 (12). (2026-08-25). https://doi.org/10.19734/j.issn.1001-3695.2026.04.0111. )

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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