Model_Library_Model_Zoo_.md

graph LR
    Model_Base_Class["Model Base Class"]
    Concrete_Recommendation_Models["Concrete Recommendation Models"]
    Input_Layer["Input Layer"]
    Sequential_Input_Layer["Sequential Input Layer"]
    Deep_Neural_Network_Layer["Deep Neural Network Layer"]
    Multi_Head_Attention_Layer["Multi-Head Attention Layer"]
    Multi_Head_Cross_Attention_Layer["Multi-Head Cross Attention Layer"]
    Backbone_Layer["Backbone Layer"]
    Concrete_Recommendation_Models -- "inherits from" --> Model_Base_Class
    Concrete_Recommendation_Models -- "composes" --> Input_Layer
    Concrete_Recommendation_Models -- "composes" --> Sequential_Input_Layer
    Concrete_Recommendation_Models -- "composes" --> Deep_Neural_Network_Layer
    Concrete_Recommendation_Models -- "composes" --> Multi_Head_Attention_Layer
    Concrete_Recommendation_Models -- "composes" --> Multi_Head_Cross_Attention_Layer
    Concrete_Recommendation_Models -- "composes" --> Backbone_Layer
    Backbone_Layer -- "composes" --> Deep_Neural_Network_Layer
    Backbone_Layer -- "composes" --> Multi_Head_Attention_Layer

Loading

Details

The Model Library (Model Zoo) subsystem is primarily defined by the easy_rec.python.model and easy_rec.python.layers packages, containing core model definitions, abstract base models, concrete recommendation model implementations, and reusable neural network layers.

Model Base Class

Serves as the foundational abstract class for all recommendation models, providing common functionalities and a standardized interface. It defines the contract for how models are built and interact within the EasyRec framework.

Related Classes/Methods:

• easy_rec.python.model.easy_rec_model

Concrete Recommendation Models

These are the concrete implementations of various deep learning recommendation model architectures (e.g., DCN, ESMM, MIND, PLE). They extend the Model Base Class and compose various layers to form complete recommendation models.

Related Classes/Methods:

• easy_rec.python.model.dcn
• easy_rec.python.model.esmm
• easy_rec.python.model.mind
• easy_rec.python.model.multi_tower_din
• easy_rec.python.model.ple
• easy_rec.python.model.rank_model

Input Layer

Responsible for processing raw input features and transforming them into appropriate tensor formats for model consumption. It handles the initial stage of data preparation within the model's graph.

Related Classes/Methods:

• easy_rec.python.layers.input_layer

Sequential Input Layer

Specifically designed for processing sequential input features, transforming them into suitable tensor formats for models that handle sequence data (e.g., user behavior sequences).

Related Classes/Methods:

• easy_rec.python.layers.seq_input_layer

Deep Neural Network Layer

A generic Deep Neural Network (DNN) layer, serving as a fundamental and widely used building block for creating dense representations and non-linear transformations within models.

Related Classes/Methods:

• easy_rec.python.layers.dnn

Multi-Head Attention Layer

Implements the multi-head attention mechanism, crucial for models processing sequential data or capturing complex relationships between different parts of the input, often used in transformer-based architectures.

Related Classes/Methods:

• easy_rec.python.layers.multihead_attention

Multi-Head Cross Attention Layer

Implements a multi-head cross-attention mechanism, enabling a model to attend to different parts of a second sequence or set of features, facilitating interaction between distinct input modalities or representations.

Related Classes/Methods:

• easy_rec.python.layers.multihead_cross_attention

Backbone Layer

Provides a flexible mechanism to define and integrate various backbone network architectures into the recommendation models. It allows for modularity in constructing the main feature processing part of a model.

Related Classes/Methods:

• easy_rec.python.layers.backbone

FAQ