You should now know how to do the following:
- Describe the purpose of recommendation systems.
- Explain the components of a recommendation system including candidate generation, scoring, and re-ranking.
- Use embeddings to represent items and queries.
- Distinguish between content-based filtering and collaborative filtering.
- Describe how matrix factorization can be used in recommendation systems.
- Explain how deep neural networks can overcome some of the limitations of matrix factorization.
- Describe a retrieval, scoring, re-ranking approach to building a recommendation system.