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xDeepFM

简介

xDeepFM模型延续了deep&cross network(参考DCN)模型的建模思想。不过,在建模显式高阶交叉特征时,采用了不同于deep&cross network的方式,文章称为compressed interaction network(CIN),并将CIN网络与深度神经网络结合,最后输入到输出层。

xdeepfm.png

配置说明

model_config {
    feature_groups {
        group_name: "wide"
        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: 'tag_category_list'
        feature_names: 'tag_brand_list'
        feature_names: 'adgroup_id'
        feature_names: 'cate_id'
        feature_names: 'campaign_id'
        feature_names: 'customer'
        feature_names: 'brand'
        feature_names: 'price'
        feature_names: 'pid'
        group_type: WIDE
    }
    feature_groups {
        group_name: "deep"
        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: 'tag_category_list'
        feature_names: 'tag_brand_list'
        feature_names: 'adgroup_id'
        feature_names: 'cate_id'
        feature_names: 'campaign_id'
        feature_names: 'customer'
        feature_names: 'brand'
        feature_names: 'price'
        feature_names: 'pid'
        group_type: DEEP
    }
    xdeepfm {
        cin {
            cin_layer_size: [64, 64]
        }
        deep {
            hidden_units: [128, 64]
        }
        final {
            hidden_units: [64, 32]
        }
    }
    metrics {
        auc {}
    }
    losses {
        binary_cross_entropy {}
    }
}
  • cin: 特征交叉层
    • cin_layer_size: cin每层要输出的维度
  • deep
    • hidden_units: dnn每一层的channel数目,即神经元的数目
  • final: 整合cross层, deep层的全连接层

示例Config

xdeepfm_criteo.config

参考论文

xDeepFM