推荐系统概览
使用集合让一切井井有条
根据您的偏好保存内容并对其进行分类。
推荐系统的一种常见架构包括
以下组件：
候选集生成
在第一个阶段，系统从一个潜在的大型语料库开始，
会生成更小的候选字词子集。例如，候选人
YouTube 生成器可将数十亿视频缩减到数百或数千个。
模型需要快速评估查询，因为存在海量数据，
语料库。一个给定的模型可能会提供多个候选生成器，每个生成器都会提名
一组不同的候选字词。
评分
接下来，另一个模型对候选模型进行评分和排名，
要向用户显示的一组项（按 10 的顺序排列）。由于
模型只评估了相对较小的项目子集，系统可以使用
依赖于其他查询的更精确的模型。
重新排名
最后，系统必须将
最终排名。例如，系统会移除用户
明确不喜欢的内容，或提高较新内容的得分。重新排名
还可以有助于确保多样性、新鲜度和公平性。
我们将在本课程中讨论各个阶段
以 YouTube 等不同推荐系统为例。
如未另行说明，那么本页面中的内容已根据知识共享署名 4.0 许可获得了许可，并且代码示例已根据 Apache 2.0 许可获得了许可。有关详情，请参阅 Google 开发者网站政策。Java 是 Oracle 和/或其关联公司的注册商标。
最后更新时间 (UTC)：2024-07-26。
[null,null,["最后更新时间 (UTC)：2024-07-26。"],[[["\u003cp\u003eRecommendation systems often use a three-stage architecture: candidate generation, scoring, and re-ranking.\u003c/p\u003e\n"],["\u003cp\u003eCandidate generation narrows down a large pool of potential recommendations to a smaller subset for further evaluation.\u003c/p\u003e\n"],["\u003cp\u003eScoring assigns relevance scores to the candidates and ranks them to identify the top recommendations.\u003c/p\u003e\n"],["\u003cp\u003eRe-ranking adjusts the initial ranking to address additional factors like user preferences, diversity, and content freshness.\u003c/p\u003e\n"]]],[],null,["# Recommendation systems overview\n\nOne common architecture for recommendation systems consists of the\nfollowing components:\n\n- candidate generation\n- scoring\n- re-ranking\n\nCandidate generation\n--------------------\n\nIn this first stage, the system starts from a potentially huge corpus and\ngenerates a much smaller subset of candidates. For example, the candidate\ngenerator in YouTube reduces billions of videos down to hundreds or thousands.\nThe model needs to evaluate queries quickly given the enormous size of the\ncorpus. A given model may provide multiple candidate generators, each nominating\na different subset of candidates.\n\nScoring\n-------\n\nNext, another model scores and ranks the candidates in order to select\nthe set of items (on the order of 10) to display to the user. Since this\nmodel evaluates a relatively small subset of items, the system can use\na more precise model relying on additional queries.\n\nRe-ranking\n----------\n\nFinally, the system must take into account additional constraints for the\nfinal ranking. For example, the system removes items that the user\nexplicitly disliked or boosts the score of fresher content. Re-ranking\ncan also help ensure diversity, freshness, and fairness.\n\nWe will discuss each of these stages over the course of the class and\ngive examples from different recommendation systems, such as YouTube.\n| **Extra Resource:** For a more comprehensive account of the technology, architecture, and models used in YouTube, see [Covington\n| et al., Deep Neural Networks for YouTube Recommendations.](https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/45530.pdf)"]]
