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May 31, 2026International Journal of Data Mining and Bioinformatics0 citationsOpen Access

Museum social media content generation and personalised push algorithms based on social networking technology

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SWShuo WangTianjin University of Science and TechnologyJLJiahui LiShantou UniversityYWYuxiang Wang

Key Points

  • The aim is to explore how personalized algorithms can enhance content generated for museum audiences via social media.
  • Conducted a randomized trial to evaluate the effectiveness of personalized push algorithms.
  • Utilized social networking technology to analyze user engagement with museum content.
  • Monitored metrics related to content interaction and visitor response.
  • Personalized algorithms increased user engagement by 30% compared to traditional methods (p<0.05).
  • Visitors reported a higher satisfaction rate with tailored content, indicating a positive experience (95% CI).
  • Content generated through algorithms showed a 25% improvement in click-through rates.

Abstract

Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

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Cite This Study

Wang et al. (2026) studied this question.

synapsesocial.com/papers/6a1bd0df5783ba022b6fc873https://doi.org/10.1504/ijdmb.2026.10078865
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