[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-08-25 UTC."],[[["\u003cp\u003eRecommendation systems predict which items a user will like based on their past behavior and preferences.\u003c/p\u003e\n"],["\u003cp\u003eThese systems use a multi-stage process: identifying potential items (candidate generation), evaluating their relevance (scoring), and refining the order of presentation (re-ranking).\u003c/p\u003e\n"],["\u003cp\u003eEmbeddings play a key role in representing items and user queries, facilitating comparisons for recommendations.\u003c/p\u003e\n"],["\u003cp\u003eTwo primary approaches for recommendation are content-based filtering (using item features) and collaborative filtering (using user similarities).\u003c/p\u003e\n"],["\u003cp\u003eDeep learning techniques enhance traditional methods like matrix factorization, enabling more complex and accurate recommendations.\u003c/p\u003e\n"]]],[],null,["# Course summary\n\n\u003cbr /\u003e\n\nYou should now know how to do the following:\n\n- Describe the purpose of recommendation systems.\n- Explain the components of a recommendation system including candidate generation, scoring, and re-ranking.\n- Use embeddings to represent items and queries.\n- Distinguish between content-based filtering and collaborative filtering.\n- Describe how matrix factorization can be used in recommendation systems.\n- Explain how deep neural networks can overcome some of the limitations of matrix factorization.\n- Describe a retrieval, scoring, re-ranking approach to building a recommendation system."]]
