August 17, 2025bit-TechOpen Access

Implementation of Content-Based Filtering in a Novel Recommendation System to Enhance User Experience

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This research demonstrates improved accuracy in user experience for digital novels, integrating content-based filtering and random forest methods.

The system achieves 94.0% accuracy in delivering personalized novel recommendations, enhancing user engagement.
Precision and recall metrics stand at 81.4% and 80.3%, indicating effective relevance in recommendations.
Features are extracted from novel attributes using TF-IDF, followed by cosine similarity for title relevance.
This hybrid approach improves personalization in digital novel platforms, boosting reader satisfaction and retention.

Sanjaya et al. (2025) studied this question.

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This research demonstrates improved accuracy in user experience for digital novels, integrating content-based filtering and random forest methods.