Abstract
Algorithm-based recommendations for precision science communication aim to address the challenge of accurately, swiftly, and efficiently extracting science information, categorizing it, and delivering targeted content in the information-saturated digital age. Intelligent algorithms rely on massive datasets for information mining, integration, and distribution. By analyzing users' information reception patterns, they enable precise, efficient, and rapid personalized recommendations. The powerful computational capabilities and information dissemination speed of big data algorithms have triggered a "technological tsunami" gradually emerging as a new variable in science popularization for grassroots communities. The era of artificial intelligence centered on big data algorithms has arrived. China's science popularization initiatives have flourished, continuously building and establishing a modern science museum system. This system, anchored by physical science museums and supported by mobile science museums, science outreach vans, and digital science museums, has become a project that benefits the people. Public science popularization services have become more balanced and effective.
Keywords
Algorithm-based RecommendationsRural AreasScience Popularization
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Cite This Article
Zhang, W. (2026). Recommendations for enhancing algorithm recommendation technology to improve the precision of science popularization. Academic Journal of Sociology and Management, 4(1), 14-21. https://doi.org/10.70393/616a736d.333432
Acknowledgments
The authors thank the editor and anonymous reviewers for their helpful comments and valuable suggestions.
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The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.
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The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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