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Designing Recommender Systems: Objectives, Architecture, and Control

7 chapters · 34 lessons

1. 1. Recommender System Architecture and Stages

   5 lessons

   Understand the multi-stage pipeline from catalog to personalized feed and the role of each component.

2. 2. Objective Functions and What You Optimize For

   5 lessons

   Learn to define and balance multiple goals beyond immediate engagement.

3. 3. Collaborative Filtering and Content-Based Methods

   4 lessons

   Apply foundational techniques for generating candidates and understanding user-item affinity.

4. 4. Embeddings, Vector Search, and Retrieval

   5 lessons

   Use embeddings to represent users and items in shared spaces for efficient candidate retrieval.

5. 5. User Control, Explainability, and Transparency

   5 lessons

   Design interfaces and features that let users understand and shape their recommendations.

6. 6. Evaluation Beyond Click-Through Rate

   5 lessons

   Measure recommender success using satisfaction, diversity, fairness, and long-term retention.

7. 7. Governance, Compliance, and Trust

   5 lessons

   Navigate regulatory requirements and build recommender systems users can trust.