Full course coming soon

Recommendation System Strategy for Content and Product Teams

7 chapters · 29 lessons

1. 1. Anatomy of a Recommendation System

   4 lessons

   Understand the core components of feed ranking systems and how they allocate attention across content candidates.

2. 2. Originality and Provenance as Ranking Assets

   4 lessons

   Learn how platforms detect and reward original content versus reposts, and why this matters for strategy.

3. 3. Reverse Engineering Platform Incentives

   4 lessons

   Decode what platforms optimize for and how to align your content strategy with those goals.

4. 4. Content Portfolio Design for Algorithmic Distribution

   5 lessons

   Build a content mix that balances trend participation, original formats, and long-term ranking power.

5. 5. Measuring and Interpreting Algorithmic Performance

   4 lessons

   Build dashboards and frameworks to detect early signals of ranking changes and content health.

6. 6. Strategic Trade-Offs and Recommendation Risk

   4 lessons

   Evaluate platform dependency, diversification strategies, and when to pivot your distribution approach.

7. 7. Applying Recommendation Thinking to Product and Community

   4 lessons

   Extend recommendation system principles to product feeds, community platforms, and internal content systems.