When music just doesn't match our vibe, can AI help? - Filip Makraduli | Vector Space Talks

Filip Makraduli explores the use of AI to improve music recommendation systems by capturing and understanding the emotional "vibe" of songs through human language and AI models. His innovative approach uses large language models to generate song descriptions and employs sentence transformers to create embeddings, which are then analyzed in vector spaces using cosine similarity to make personalized song recommendations. Makraduli demonstrates the potential of this system with a live demo, showcasing its ability to create Spotify playlists tailored to specific moods. His work is part of a broader interest in leveraging AI for more nuanced and personalized user experiences, reflecting his expertise in data science and machine learning, particularly in unconventional recommender systems.