Interpretable Hybrid Recommendation System For Personalized Skincare Products

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dc.contributor.advisorBérczes, Tamás Márton
dc.contributor.authorMustapha, Al-Amin Oluwatimilehin
dc.contributor.departmentDE--Informatikai Kar
dc.date.accessioned2026-02-12T18:07:53Z
dc.date.available2026-02-12T18:07:53Z
dc.date.created2025-11-04
dc.description.abstractThis thesis develops an interpretable hybrid recommendation system for personalized skincare product selection. The system combines ingredient-based analysis with user review sentiment extraction to generate balanced, data-driven recommendations. Using Natural Language Processing techniques, both lexicon-based (VADER) and AI-driven (GPT) methods are applied to assess user sentiment, while dermatological ingredient data are scored using a logarithmic weighting scheme. A tunable parameter (α) controls the influence between objective formulation data and subjective user feedback. Experiments show stable and interpretable ranking behavior, with optimal performance around α = 0.6. The results demonstrate the feasibility of transparent, AI-driven skincare recommendation systems that integrate scientific formulation knowledge with real-world user experience.
dc.description.courseMérnökinformatikus
dc.description.degreeBSc/BA
dc.format.extent50
dc.identifier.urihttps://hdl.handle.net/2437/404397
dc.language.isoen
dc.rights.infoHozzáférhető a 2022 decemberi felsőoktatási törvénymódosítás értelmében.
dc.subjectRecommendation Systems
dc.subjectNatural Language Processing
dc.subjectSentiment Analysis
dc.subjectArtificial Intelligence
dc.subject.dspaceInformatics::Computer Science
dc.titleInterpretable Hybrid Recommendation System For Personalized Skincare Products
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