AutoInt¶
简介¶
Automatic Feature Interaction Learning via Self-Attentive Neural Networks(AutoInt)通过将特征都映射到相同的低维空间中,然后利用带有残差连接的 Multi-head Self-Attention 机制显示构造高阶特征,对低维空间中的特征交互进行显式建模,有效提升了CTR预估的准确率。 注意:AutoInt 模型要求所有输入特征的 embedding_dim 保持一致。
配置说明¶
model_config: {
model_class: 'AutoInt'
feature_groups: {
group_name: 'all'
feature_names: 'user_id'
feature_names: 'cms_segid'
feature_names: 'cms_group_id'
feature_names: 'age_level'
feature_names: 'pvalue_level'
feature_names: 'shopping_level'
feature_names: 'occupation'
feature_names: 'new_user_class_level'
feature_names: 'adgroup_id'
feature_names: 'cate_id'
feature_names: 'campaign_id'
feature_names: 'customer'
feature_names: 'brand'
feature_names: 'price'
feature_names: 'pid'
feature_names: 'tag_category_list'
feature_names: 'tag_brand_list'
wide_deep: DEEP
}
autoint {
multi_head_num: 2
multi_head_size: 32
interacting_layer_num: 3
l2_regularization: 1e-6
}
embedding_regularization: 1e-4
}
model_class: ‘AutoInt’, 不需要修改
feature_groups: 配置一个名为’all’的feature_group。
autoint: autoint相关的参数
model_dim: 与特征的embedding_dim保持一致
multi_head_size: Multi-head Self-attention 中的 head size,默认为1
interacting_layer_num: 交叉层的层数,建议设在1到5之间,默认为1
l2_regularization: L2正则,防止 overfit
embedding_regularization: 对embedding部分加regularization,防止overfit