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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 42, 2025 No. 1
78-85

Neural collaborative filtering recommendation model for de-exposure bias based on fused rewards

Li Peng
Li Xiaoshan
Zhu Xinru
School of Management, Harbin University of Commerce, Harbin 150028, China
DOI: 10.19734/j.issn.1001-3695.2024.05.0184

Abstract

In recommendation systems, strong exposure bias caused by sparse interaction data and uneven exposure tends to concentrate recommendations on highly exposed items, neglecting the potential value of low-exposure items, thus limiting user choices and diminishing user experience. To address this issue, this paper proposed a model that integrated neural collaborative filtering and the linear upper confidence bound(LinUCB) algorithm to mitigate exposure bias. Firstly, the model used neural collaborative filtering to analyze interaction data between users and items, learning their features and capturing latent preferences. Secondly, it introduced the LinUCB algorithm, embedding its generated reward feature into the neural collaborative filtering model to enhance the exploration capabilities for low-exposure items. Finally, experiments conducted on the MovieLens-100K and MovieLens-1M datasets demonstrated that this model increased exposure by approximately 60% compared to traditional neural collaborative filtering models. This enhancement suggests that the proposed method effectively mitigates exposure bias and improves both the accuracy and fairness of recommendations, thereby validating the effectiveness of the model.

Foundation Support

2023年哈尔滨商业大学青年科研创新人才培育计划资助项目(2023-KYYWF-1001)
黑龙江省博士后科研启动金资助项目(BS0053)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.05.0184
Publish at: Application Research of Computers Printed Article, Vol. 42, 2025 No. 1
Section: Algorithm Research & Explore
Pages: 78-85
Serial Number: 1001-3695(2025)01-011-0078-08

Publish History

[2025-01-05] Printed Article

Cite This Article

李鹏, 李晓珊, 朱心如. 基于融合奖励的神经协同过滤去曝光偏差推荐模型 [J]. 计算机应用研究, 2025, 42 (1): 78-85. (Li Peng, Li Xiaoshan, Zhu Xinru. Neural collaborative filtering recommendation model for de-exposure bias based on fused rewards [J]. Application Research of Computers, 2025, 42 (1): 78-85. )

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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