Course summary

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.