Faulinda Ely Nastiti

Comparison of TF-IDF and Sentence-Transformer NLP Methods for a Perfume Recommendation System Based on User Descriptions with Streamlit Visualization

Findings confirm that semantic embedding methods provide superior ranking quality and cross-lingual robustness, offering a scalable and translation-free solution applicable to multilingual product recommendation systems in commercial settings.