-
Notifications
You must be signed in to change notification settings - Fork 346
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
- Loading branch information
1 parent
871a40e
commit 17c13e5
Showing
17 changed files
with
526 additions
and
99 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Loading
Sorry, something went wrong. Reload?
Sorry, we cannot display this file.
Sorry, this file is invalid so it cannot be displayed.
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,118 @@ | ||
| # AITM | ||
|
|
||
| ### 简介 | ||
|
|
||
| 在推荐场景里，用户的转化链路往往有多个中间步骤（曝光->点击->转化），AITM是一种多任务模型框架，充分利用了链路上各个节点的样本，提升模型对后端节点转化率的预估。 | ||
|
|
||
|  | ||
|
|
||
| 1. (a) Expert-Bottom pattern。如 [MMoE](mmoe.md) | ||
| 1. (b) Probability-Transfer pattern。如 [ESMM](esmm.md) | ||
| 1. (c) Adaptive Information Transfer Multi-task (AITM) framework. | ||
|
|
||
| 两个特点： | ||
|
|
||
| 1. 使用Attention机制来融合多个目标对应的特征表征； | ||
| 1. 引入了行为校正的辅助损失函数。 | ||
|
|
||
| ### 配置说明 | ||
|
|
||
| ```protobuf | ||
| model_config { | ||
| model_name: "AITM" | ||
| model_class: "MultiTaskModel" | ||
| feature_groups { | ||
| group_name: "all" | ||
| feature_names: "user_id" | ||
| feature_names: "cms_segid" | ||
| ... | ||
| feature_names: "tag_brand_list" | ||
| wide_deep: DEEP | ||
| } | ||
| backbone { | ||
| blocks { | ||
| name: "mlp" | ||
| inputs { | ||
| feature_group_name: "all" | ||
| } | ||
| keras_layer { | ||
| class_name: 'MLP' | ||
| mlp { | ||
| hidden_units: [512, 256] | ||
| } | ||
| } | ||
| } | ||
| } | ||
| model_params { | ||
| task_towers { | ||
| tower_name: "ctr" | ||
| label_name: "clk" | ||
| loss_type: CLASSIFICATION | ||
| metrics_set: { | ||
| auc {} | ||
| } | ||
| dnn { | ||
| hidden_units: [256, 128] | ||
| } | ||
| use_ait_module: true | ||
| weight: 1.0 | ||
| } | ||
| task_towers { | ||
| tower_name: "cvr" | ||
| label_name: "buy" | ||
| losses { | ||
| loss_type: CLASSIFICATION | ||
| } | ||
| losses { | ||
| loss_type: ORDER_CALIBRATE_LOSS | ||
| } | ||
| metrics_set: { | ||
| auc {} | ||
| } | ||
| dnn { | ||
| hidden_units: [256, 128] | ||
| } | ||
| relation_tower_names: ["ctr"] | ||
| use_ait_module: true | ||
| ait_project_dim: 128 | ||
| weight: 1.0 | ||
| } | ||
| l2_regularization: 1e-6 | ||
| } | ||
| embedding_regularization: 5e-6 | ||
| } | ||
| ``` | ||
|
|
||
| - model_name: 任意自定义字符串，仅有注释作用 | ||
|
|
||
| - model_class: 'MultiTaskModel', 不需要修改, 通过组件化方式搭建的多目标排序模型都叫这个名字 | ||
|
|
||
| - feature_groups: 配置一组特征。 | ||
|
|
||
| - backbone: 通过组件化的方式搭建的主干网络，[参考文档](../component/backbone.md) | ||
|
|
||
| - blocks: 由多个`组件块`组成的一个有向无环图（DAG），框架负责按照DAG的拓扑排序执行个`组件块`关联的代码逻辑，构建TF Graph的一个子图 | ||
| - name/inputs: 每个`block`有一个唯一的名字（name），并且有一个或多个输入(inputs)和输出 | ||
| - keras_layer: 加载由`class_name`指定的自定义或系统内置的keras layer，执行一段代码逻辑；[参考文档](../component/backbone.md#keraslayer) | ||
| - mlp: MLP模型的参数，详见[参考文档](../component/component.md#id1) | ||
|
|
||
| - model_params: AITM相关的参数 | ||
|
|
||
| - task_towers 根据任务数配置task_towers | ||
| - tower_name | ||
| - dnn deep part的参数配置 | ||
| - hidden_units: dnn每一层的channel数目，即神经元的数目 | ||
| - use_ait_module: if true 使用`AITM`模型；否则，使用[DBMTL](dbmtl.md)模型 | ||
| - ait_project_dim: 每个tower对应的表征向量的维度，一般设为最后一个隐藏的维度即可 | ||
| - 默认为二分类任务，即num_class默认为1，weight默认为1.0，loss_type默认为CLASSIFICATION，metrics_set为auc | ||
| - loss_type: ORDER_CALIBRATE_LOSS 使用目标依赖关系校正预测结果的辅助损失函数，详见原始论文 | ||
| - 注：label_fields需与task_towers一一对齐。 | ||
| - embedding_regularization: 对embedding部分加regularization，防止overfit | ||
|
|
||
| ### 示例Config | ||
|
|
||
| - [AITM_demo.config](https://github.com/alibaba/EasyRec/blob/master/samples/model_config/aitm_on_taobao.config) | ||
|
|
||
| ### 参考论文 | ||
|
|
||
| [AITM: Modeling the Sequential Dependence among Audience Multi-step Conversions with Multi-task Learning in Targeted Display Advertising](https://arxiv.org/pdf/2105.08489.pdf) |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
|
|
@@ -7,5 +7,6 @@ | |
| esmm | ||
| mmoe | ||
| dbmtl | ||
| aitm | ||
| ple | ||
| simple_multi_task | ||
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Oops, something went wrong.