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Algorithm Research & Explore
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1108-1114

Recommendation algorithm based on smooth interpolation and adaptive similarity matrix

Gao Meizhu
Yu Wanjun
Chen Ying
School of Computer Science & Information Engineering, Shanghai Institute of Technology, Shanghai 201418, China

Abstract

Aimed at the problems where the collaborative filtering recommendation overly relied on common item ratings and lacked dense interaction data, and where sharing the same similarity matrix across different time periods could not accurately measure user similarity, this paper proposed a recommendation algorithm based on smooth interpolation and adaptive similarity matrix. Firstly, based on linear interpolation technique, dynamic intervals set the mean and standard deviation, and the sigmoid function smoothly adjusted the original ratings to eliminate differences in user rating habits. Next, the temporal transformation function quantified the different dynamic patterns and forgetting behaviors that user preferences followed, enhancing the representation of user preferences. Finally, the label semantics, label quality, temporal transformation function, and relative rating diffe-rence entropy constructed the label perception mechanism and global rating mechanism, and the generated similarity matrix was used to reconstruct the user-adaptive similarity matrix. The simulation results show that, compared to other baseline algorithms, the proposed algorithm achieves the best recommendation performance. The recall rate improved by 5.27 and 4.73 percentage points, and the normalized discounted cumulative gain(NDCG) improved by 6.67 and 5.90 percentage points, which verifies the effectiveness of the algorithm.

Foundation Support

国家自然科学基金资助项目(61976140)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.09.0335
Publish at: Application Research of Computers Printed Article, Vol. 42, 2025 No. 4
Section: Algorithm Research & Explore
Pages: 1108-1114
Serial Number: 1001-3695(2025)04-019-1108-07

Publish History

[2025-04-05] Printed Article

Cite This Article

高美珠, 于万钧, 陈颖. 基于平滑插值和自适应相似矩阵的推荐算法 [J]. 计算机应用研究, 2025, 42 (4): 1108-1114. (Gao Meizhu, Yu Wanjun, Chen Ying. Recommendation algorithm based on smooth interpolation and adaptive similarity matrix [J]. Application Research of Computers, 2025, 42 (4): 1108-1114. )

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.

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