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Embedding-Based Recommendation with Similarity Scoring

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Overview

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.

Syllabus

Unit 1: 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

Unit 2: Cosine Similarity Recommendations

Explore the Cosine Similarity Recommendation Flow
Run a Similarity-Based Recommendation Test
Why Are Listened Tracks Showing Up?
Build the Brain: Recommend by Similarity

Unit 3: Clustering Music Tracks

Get to Know the Clustering Engine
Cluster and Observe: Diagnostic Flow for Music Clustering
Debug the Beat: Fix the Broken Clustering
Clustering Music Tracks Using KMeans and Embeddings

Unit 4: Embedding Recommendation Endpoints

Explore the Embedding-Based API Endpoints
Build the Embedding-Based Recommendation Endpoint
Group Tracks by Clusters in Your API
Reveal the User’s Musical Soul — Profile Vector API

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