[[["容易理解","easyToUnderstand","thumb-up"],["確實解決了我的問題","solvedMyProblem","thumb-up"],["其他","otherUp","thumb-up"]],[["缺少我需要的資訊","missingTheInformationINeed","thumb-down"],["過於複雜/步驟過多","tooComplicatedTooManySteps","thumb-down"],["過時","outOfDate","thumb-down"],["翻譯問題","translationIssue","thumb-down"],["示例/程式碼問題","samplesCodeIssue","thumb-down"],["其他","otherDown","thumb-down"]],["上次更新時間:2025-01-13 (世界標準時間)。"],[[["Recommendation systems predict which items a user will like based on their past behavior and preferences."],["These systems use a multi-stage process: identifying potential items (candidate generation), evaluating their relevance (scoring), and refining the order of presentation (re-ranking)."],["Embeddings play a key role in representing items and user queries, facilitating comparisons for recommendations."],["Two primary approaches for recommendation are content-based filtering (using item features) and collaborative filtering (using user similarities)."],["Deep learning techniques enhance traditional methods like matrix factorization, enabling more complex and accurate recommendations."]]],[]]