检查您的理解情况
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。
为什么要使用推荐系统?
提供商品推荐引擎可让您更轻松地浏览内容。
此外,出色的推荐系统还可以帮助用户找到他们想不到的商品。
你认为必须在一切事物上运用机器学习技术。
看起来似乎是这样,但实际上,使用机器学习有更好的理由。
您希望将用户引导至赞助商内容。
哎呀,这不是使用任何机器学习解决方案的好理由。
Recommender 系统的主要组件有哪些?
候选人生成、评分和重新排名
干得漂亮!这些是任何推荐系统的三个主要组成部分。
嵌入、相似度指标和服务
这些元素与推荐系统相关,但它们不是主要组件。
矩阵分解、DNN 和重新排名
虽然重新排名是一个组件,但矩阵分解和 DNN 是候选生成器的类型。
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 enhance content browsing and help users discover new items.\u003c/p\u003e\n"],["\u003cp\u003eRecommender systems are built with three primary components: candidate generation, scoring, and re-ranking.\u003c/p\u003e\n"],["\u003cp\u003eWhile machine learning is often associated with recommendation systems, using it solely to promote sponsored items is not recommended.\u003c/p\u003e\n"]]],[],null,["# Check your understanding\n\n\u003cbr /\u003e\n\nWhy would you use recommendation systems? \nHaving a recommendation engine makes browsing content easier. \nPlus, a great recommendation system helps users find things they wouldn't have thought to look for on their own. \nYou think you have to sprinkle ML on everything. \nIt might seem that way, but in reality, there are much better reasons to use ML. \nYou want to direct users to sponsored items. \nYikes, this is not a great reason to use any ML solution. \nWhat are the primary components of a recommender system? \ncandidate generation, scoring, and re-ranking \nNicely done! These are the three primary components of any recommendation system. \nembedding, similarity metrics, and serving \nThese elements are related to recommendation systems, but they are not primary components. \nmatrix factorization, DNN, and reranking \nWhile re-ranking is a component, matrix factorization and DNN are types of candidate generators."]]