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  • https://doi.org/10.4018/ijkm.406088Copy DOI Icon

Enhancing Personalized Early Childhood Reading Recommendations

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Abstract

This study aims to address three core issues in children's literature recommendation systems for ages 0–6: mismatched content with children's cognitive development, lack of integrated evaluation for educational value and safety, and algorithm “black box” undermining parental/educator trust. To resolve these, it proposes a big data-driven intelligent system integrating multi-modal data, age-aware dynamic modeling, a hybrid recommendation algorithm, an educational evaluation framework, multi-level safety filtering, and explainable AI for transparency. A controlled experiment showed the experimental group had 26.9–33.3% higher recommendation accuracy, 22.9–34.9% better educational goal achievement, a 26.7-percentage-point lower content safety error rate, and significant long-term engagement growth. This system overcomes traditional limitations, providing a scalable framework for integrating big data and AI into early childhood literacy knowledge management, and insights for personalized, safe, education-aligned recommendations in educational knowledge systems.

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