2022 ACML ACML 2022

BeautifAI - Personalised Occasion-based Makeup Recommendation

Abstract

With the global metamorphosis of the beauty industry and the rising demand for beauty products worldwide, the need for a robust makeup recommendation system has never been more. Despite the significant advancements made towards personalised makeup recommendation, the current research still falls short of incorporating the context of occasion and integrating feedback for users. In this work, we propose BeautifAI, a novel recommendation system, delivering personalised occasion-oriented makeup recommendations to users. The proposed work’s novel contributions, including incorporating occasion context to makeup recommendation and a region-wise method using neural embeddings, set our system apart from the current work in makeup recommendation. We also propose real-time makeup previews and continuous makeup feedback to provide a more personalised and interactive experience to users.

🌉 Interdisciplinary Bridge — Artificial Intelligence and Data Science & Analytics and Machine Learning
🧭 Keyword Pioneer — makeup recommendation
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Security & Privacy, Speech & Audio