Computer Science

Computer Science ›› 2026, Vol. 53 ›› Issue (2): 152-160.doi: 10.11896/jsjkx.241200177

• Database & Big Data & Data Science • Previous Articles     Next Articles

News Recommendation Algorithm Based on User Static and Dynamic Interests and DenoisedImplicit Negative Feedback

WEI Jinsheng1,2, ZHOU Su1, LU Guanming1,2 , DING Jiawei1   

  1. 1 School of Communication and Information Engineering,Nanjing University of Posts and Telecommunications,Nanjing 210003,China
    2 Jiangsu Key Laboratory of Intelligent Information Processing and Communication Technology,Nanjing University of Posts and Telecommunications,Nanjing 210003,China
  • Received:2024-12-24 Revised:2025-05-10 Published:2026-02-10
  • About author:WEI Jinsheng,born in 1995,Ph.D,lecturer.His main research interests include multimedia information proce-ssing,machine learning,intelligent re-commendation and affective computing.
    LU Guanming,born in 1965,Ph.D,professor,Ph.D supervisor.His main research interests include intelligent information processing,intelligent recommendation and machine learning.
  • Supported by:
    National Natural Science Foundation of China(72074038),Natural Science Foundation of the Higher Education Institutions of Jiangsu Province(24KJB520022),Nanjing Science and Technology Innovation Foundation for Overseas Students (RK002NLX23004),Natural Science Research Start-up Foundation of Recruiting Talents of Nanjing University of Posts and Telecommunications(NY223030) and Postgraduate Research & Practice Innovation Program of Jiangsu Province(KYCX22_0950).

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Abstract: In the existing news recommendation system,the news that users have not clicked on in the past is usually regarded as implicit negative feedback,and modelling the implicit negative feedback can guide the recommendation model to filter out the news that users are not interested in.Because there may be content of interest to users in the news that is not clicked,that is,there is preference noise,which leads to interference with modelling implicit negative feedback.In addition,due to the diversification and variability of user interests,the existing news recommendation system often has the problem of an “information cocoon”.To solve the above problems,this paper proposes a news recommendation algorithm based on users’ static and dynamic interests and the denoised implicit negative feedback.By fusing and modeling the static and dynamic interests of users,and the denoised implicit negative feedback of static and dynamic interests,the dynamically updated user preference model is constructed.Firstly,a static interest denoising module based on orthogonal mapping is constructed to denoise the implicit negative feedback in static interest.Then,the GRU and orthogonal mapping are fused to construct a dynamic interest denoising module based on the improved GRU,which fully models the user’s interest change and realizes the denoising of the implicit negative feedback in the dynamic interest.Finally,by introducing contrastive learning technology,the model’s ability to distinguish between implicit positive and negative feedback is enhanced to improve the performance of personalized news recommendations.Experiments on the MIND dataset show that compared with the baseline method,the model improves by 1.18%,1.84%,2.75% and 1.67% on the four evaluation in-dexes of AUC,MRR,NDCG@5 and NDCG@10,respectively,which verifies the effectiveness of the proposed model.

Key words: News recommendation, Information cocoon, Orthogonal mapping, Gated recurrent unit, Contrastive learning

CLC Number: 

  • TP391
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