现在，您应该了解如何执行以下操作：
- 说明推荐系统的用途。
- 说明推荐系统的组成部分，包括候选生成、评分和重新排名。
- 使用嵌入来表示项和查询。
- 区分基于内容的过滤和协同过滤。
- 说明如何在推荐系统中使用矩阵分解。
- 说明深度神经网络如何克服矩阵分解的一些局限性。
- 描述构建推荐系统的检索、评分和重新排名方法。
课程总结
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最后更新时间 (UTC)：2025-01-13。
[null,null,["最后更新时间 (UTC)：2025-01-13。"],[[["\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."]]