Full course coming soon

Building Privacy-Aware Recommendation Systems

7 chapters · 34 lessons

1. 1. Recommendation System Fundamentals and Architecture

   5 lessons

   Understand the core pipeline from user events through candidate generation, ranking, and feedback loops.

2. 2. Collaborative and Content-Based Filtering

   5 lessons

   Implement core recommendation algorithms using user behavior patterns and item attributes.

3. 3. Embeddings and Vector Retrieval

   5 lessons

   Use semantic embeddings and vector databases to find relevant items beyond keyword matching.

4. 4. Data Minimization and Purpose Limitation

   4 lessons

   Design telemetry collection that captures only what your recommendation feature actually needs.

5. 5. Privacy-Preserving Recommendation Techniques

   5 lessons

   Apply differential privacy, federated learning, and on-device ranking to protect user data.

6. 6. Feedback Loops and Model Improvement

   5 lessons

   Build learning systems that improve recommendations without accumulating unnecessary user data.

7. 7. Deployment and System Design Tradeoffs

   5 lessons

   Architect recommendation systems across mobile, edge, and cloud with latency and privacy constraints.