Embedding-Based Recommendation with Similarity Scoring

Dive into the world of smart recommendations! You'll learn to transform music track features like genre, mood, and tempo into numerical vectors (embeddings). Then, you'll create user profiles based on their listening history and use cosine similarity to find and suggest new tracks they might love. You'll also explore clustering techniques to group similar songs.