Software Engineering
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.
Pandas
Python
sklearn
4 lessons
16 practices
2 hours
Server-Side Programming
Course details
Encoding Tracks and User Profiles into Vector Space
Explore Track and User Embedding Logic
Build the Preprocessor for Track Feature Embeddings
Generate Embeddings for All Tracks
Generate a User Profile Vector from Listening History
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