推荐系统是机器学习在商业中最成功和最广泛的应用之一。您可以使用推荐系统来帮助用户在大量内容中找到感兴趣的内容。例如，Google Play 商店提供数百万个应用，YouTube 提供数十亿个视频，并且每天都会新增更多应用和视频。用户可以使用搜索功能查找新内容，但受其所用的搜索字词的限制。推荐系统可以推荐用户自己可能未曾想到要搜索到的内容。如需了解详情，请参阅推荐系统概览。
[[["易于理解","easyToUnderstand","thumb-up"],["解决了我的问题","solvedMyProblem","thumb-up"],["其他","otherUp","thumb-up"]],[["很难理解","hardToUnderstand","thumb-down"],["信息或示例代码不正确","incorrectInformationOrSampleCode","thumb-down"],["没有我需要的信息/示例","missingTheInformationSamplesINeed","thumb-down"],["翻译问题","translationIssue","thumb-down"],["其他","otherDown","thumb-down"]],["最后更新时间 (UTC)：2025-04-14。"],[[["Recommendation systems are widely used in businesses to help users discover content they might not find through search, utilizing machine learning to suggest relevant items."],["Content-based filtering and collaborative filtering are two primary methods used in recommendation systems, with the former focusing on item similarity and the latter on user similarities."],["Matrix factorization models, which are a type of collaborative filtering, map user-item interactions to a matrix and aim to predict user preferences by filling in missing data, using latent factors to simplify the matrix."],["For more advanced recommendation systems, deep neural network (DNN) and Wide-and-Deep models can be used, incorporating query and item features to enhance the accuracy and relevance of recommendations."],["Basic machine learning knowledge can improve the effectiveness of recommendation models, and resources are recommended to help develop familiarity with machine learning techniques."]]],[]]
