推薦系統是機器學習在商業上最成功也最廣泛的應用。您可以利用推薦系統，協助使用者找到絕佳內容，避免遭到巨量內容淹沒。舉例來說，Google Play 商店提供數百萬個應用程式，而 YouTube 上則有數十億部影片，應用程式和影片的數量每天都在增加。使用者可透過搜尋找到新內容，但這會受限於所用的搜尋字詞。推薦系統可推薦使用者可能沒想過要搜尋的內容。詳情請參閱「推薦系統總覽」。
[[["容易理解","easyToUnderstand","thumb-up"],["確實解決了我的問題","solvedMyProblem","thumb-up"],["其他","otherUp","thumb-up"]],[["難以理解","hardToUnderstand","thumb-down"],["資訊或程式碼範例有誤","incorrectInformationOrSampleCode","thumb-down"],["缺少我需要的資訊/範例","missingTheInformationSamplesINeed","thumb-down"],["翻譯問題","translationIssue","thumb-down"],["其他","otherDown","thumb-down"]],["上次更新時間：2025-07-09 (世界標準時間)。"],[[["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."]]],[]]
