现在，您对如何执行以下操作应该有了更深入的了解：
- 描述推荐系统的用途。
- 了解 Recommender 系统的组成部分，包括 候选集生成、评分和重新排名。
- 使用嵌入来表示项和查询。
- 更深入地了解常用技术 候选字词生成模型。
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)：2024-08-13。
[null,null,["最后更新时间 (UTC)：2024-08-13。"],[[["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."]]],[]]