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.
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Last updated 2025-02-27 UTC.
[null,null,["Last updated 2025-02-27 UTC."],[[["Recommendation systems predict which items a user will like based on their past behavior and preferences."],["These systems use a multi-stage process: identifying potential items (candidate generation), evaluating their relevance (scoring), and refining the order of presentation (re-ranking)."],["Embeddings play a key role in representing items and user queries, facilitating comparisons for recommendations."],["Two primary approaches for recommendation are content-based filtering (using item features) and collaborative filtering (using user similarities)."],["Deep learning techniques enhance traditional methods like matrix factorization, enabling more complex and accurate recommendations."]]],[]]