such that similar inputs have similar representations. We will call this representation an Embedding. An embedding is a vector of features that represent aspects of an input numerically. It must fulfill the given animal. In Table 4-1 we manually assigned values for the cute and dangerous features for six animals2, and we are calling the tuple of these two features an embedding, where the two features embeddings. Animal Embedding (cute, dangerous) dog (0.85, 0.05) cat (0.95, 0.05) snake (0.01, 0.9) bear (0.5, 0.95) raccoon (0.5, 0.5) mouse (0.01, 0.2) Table 4-1: A table consisting of embeddings

§22. Methods §23. Interfaces - value boxes used to do reflection and polymorphism. §24. Type Embedding - type extension in the Go way. §25. Type-Unsafe Pointers §26. Generics - use and read composite related confusions. 5. Clarifies several ambiguities which exist in the Go specification, such as embedding rules, promoted method value evaluation, and panic/recover mechanism. 6. Makes several summary reflection through interfaces. pointers. function closures. methods. deferred function calls. type embedding. type deduction. memory safety. automatic garbage collection. great cross-platform compatibility

针对推荐特点的深度优化，达到业界先 进⽔平 推荐系统的核⼼特点 � Feature 1（基本特点） 1.1 User与推荐系统交互，7*24⼩时 流式学习 1.2 Item和User新增，离开/遗忘， Embedding空间动态变化。 短期命中的⾼频key随时间缓慢变化 少量的⾼频key占据了主要访问需求 ⼀段时间样 本命中的 unique key ID/tag/交叉特征 (全量为：亿，千亿) ⼩特征 User Item特征 ⽤户反馈 Item推荐 Embedding参数 本⼩时访问过的key 上⼩时访问过的key 访 问 百 分 ⽐ 时间（⼩ 时） � Feature 2（数据的时空特点） 2.1 短时间内只有部分item和user被 命中，只有部分参数被⽤到 � Feature 3（机器学习的特点） Embedding以稀疏的⽅式表达信息 ⼤规模推荐模型深度学习系统基本解决维度 参数更新 查询Sparse Table 查询Dense Tensor Reader Learner Worker 返回参数 Request Handler Parameter Server 查询Sparse Table 查询Dense Tensor 更新参数 � 常规训练流⽔线 样本读取 样本解析 参数拉取 参数更新 查询Sparse Table 查询Dense Tensor

Self-Attention 的集成， h 代表头的个数。其 中 Self-Attention 的计算公式如下： 其中，Q 代表查询，K 代表键，V 代表数值。 在我们的应用实践中，原始输入是一系列 Embedding 向量构成的矩阵 E ，矩阵 E 首先通过线性投影： 得到三个矩阵： 然后将投影后的矩阵输入到 Multi-Head Attention。计算公式如下： Point-wise Feed-Forward 保留将稠密特征和离散特征的 Embedding 送入到 MLP 网络，以隐式的方式 学习其非线性表达。 ● Transformer Layer 部分，不是送入所有特征的 Embedding，而是基于人工经 验选择了部分特征的 Embedding，第一点是因为美团搜索场景特征的维度高， 全输入进去会提高模型的复杂度，导致训练和预测都很慢；第二点是，所有特征 的 Embedding 维度不完全相同，也不适合一起输入到 Transformer Layer 。 Embedding Layer 部分：众所周知在 CTR 预估中，除了大规模稀疏 ID 特征，稠 密类型的统计特征也是非常有用的特征，所以这部分将所有的稠密特征和稀疏 ID 特 征都转换成 Embedding 表示。 Transformer 部分：针对用户行为序列、商户 、品类 、地理位置等 Embedding 表示，使用 Transformer Layer

1.12 1.13 1.14 1.15 1.16 1.17 1.18 1.19 1.20 1.21 1.22 1.23 1.24 1.25 1.26 1.27 Table of Contents Introduction Legal Notice Preface Project Info Messaging Concepts Architecture Graceful Server Shutdown Libaio Native Libraries Thread management Logging REST Interface Embedding Apache ActiveMQ Artemis Apache Karaf Spring Integration AeroGear Integration VertX Integration Apache ActiveMQ Artemis servers in your own application. For more information on embedding Apache ActiveMQ Artemis, see Embedding Apache ActiveMQ Artemis. Apache ActiveMQ Artemis integrated with a Java EE

1.12 1.13 1.14 1.15 1.16 1.17 1.18 1.19 1.20 1.21 1.22 1.23 1.24 1.25 1.26 1.27 Table of Contents Introduction Legal Notice Preface Project Info Messaging Concepts Architecture Graceful Server Shutdown Libaio Native Libraries Thread management Logging REST Interface Embedding Apache ActiveMQ Artemis Apache Karaf Spring Integration AeroGear Integration VertX Integration Apache ActiveMQ Artemis servers in your own application. For more information on embedding Apache ActiveMQ Artemis, see Embedding Apache ActiveMQ Artemis. Apache ActiveMQ Artemis integrated with a Java EE

1.12 1.13 1.14 1.15 1.16 1.17 1.18 1.19 1.20 1.21 1.22 1.23 1.24 1.25 1.26 1.27 Table of Contents Introduction Legal Notice Preface Project Info Messaging Concepts Architecture Graceful Server Shutdown Libaio Native Libraries Thread management Logging REST Interface Embedding Apache ActiveMQ Artemis Apache Karaf Spring Integration AeroGear Integration VertX Integration Apache ActiveMQ Artemis servers in your own application. For more information on embedding Apache ActiveMQ Artemis, see Embedding Apache ActiveMQ Artemis. Apache ActiveMQ Artemis integrated with a Java EE

1.12 1.13 1.14 1.15 1.16 1.17 1.18 1.19 1.20 1.21 1.22 1.23 1.24 1.25 1.26 1.27 Table of Contents Introduction Legal Notice Preface Project Info Messaging Concepts Architecture Graceful Server Shutdown Libaio Native Libraries Thread management Logging REST Interface Embedding Apache ActiveMQ Artemis Apache Karaf Spring Integration AeroGear Integration VertX Integration Apache ActiveMQ Artemis servers in your own application. For more information on embedding Apache ActiveMQ Artemis, see Embedding Apache ActiveMQ Artemis. Apache ActiveMQ Artemis integrated with a Java EE

1.12 1.13 1.14 1.15 1.16 1.17 1.18 1.19 1.20 1.21 1.22 1.23 1.24 1.25 1.26 1.27 Table of Contents Introduction Legal Notice Preface Project Info Messaging Concepts Architecture Graceful Server Shutdown Libaio Native Libraries Thread management Logging REST Interface Embedding Apache ActiveMQ Artemis Apache Karaf Spring Integration AeroGear Integration VertX Integration Apache ActiveMQ Artemis servers in your own application. For more information on embedding Apache ActiveMQ Artemis, see Embedding Apache ActiveMQ Artemis. Apache ActiveMQ Artemis integrated with a Java EE

i. Introduction ii. Defining Middleware iii. Registering Middleware iv. Terminable Middleware Table of Contents iii. Controllers i. Introduction ii. Basic Controllers iii. Controller Middleware Localization vi. Overriding Package Language Files xv. Mail i. Configuration ii. Basic Usage iii. Embedding Inline Attachments iv. Queueing Mail v. Mail & Local Development xvi. Package Development i simple corresponding views, which are located at resources/views/auth . In addition, a "users" table migration has been included with the framework. Including these simple resources allows rapid development