SIGNATURE WORK
CONFERENCE & EXHIBITION 2024

This study presents an enhanced article recommendation system utilizing a genetic algorithm to finely tune the balance between relevance and diversity. Altering conventional similarity assessment methods and incorporating a diversity coefficient, the research tackles the prevalent issue of narrow content scope in content-based systems. The approach entails detailed data processing and feature extraction to refine recommendation quality and efficiency. Empirical results, evidenced by significant ANOVA outcomes for both relevance and diversity (P < .01), affirm the model’s efficacy in delivering a more engaging and varied content selection to users.