docs: forbidden link jump
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@@ -273,5 +273,5 @@ OK, 这就是AutoInt比较核心的部分了,当然,上面自注意部分
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**参考资料**:
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* [AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks](https://link.zhihu.com/?target=https%3A//arxiv.org/abs/1810.11921)
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* [AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks](https://arxiv.org/abs/1810.11921)
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* [AutoInt:基于Multi-Head Self-Attention构造高阶特征](https://zhuanlan.zhihu.com/p/60185134)
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@@ -138,7 +138,7 @@ def ESSM(dnn_feature_columns, task_type='binary', task_names=['ctr', 'ctcvr'],
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测试数据集:
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adult:[https://archive.ics.uci.edu/ml/datasets/census+income](https://link.zhihu.com/?target=https%3A//archive.ics.uci.edu/ml/datasets/census%2Bincome)
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adult:[https://archive.ics.uci.edu/ml/datasets/census+income](https://archive.ics.uci.edu/dataset/20/census+income)
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将里面两个特征转为label,完成两个任务的预测:
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@@ -157,6 +157,6 @@ https://www.zhihu.com/question/475787809
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https://zhuanlan.zhihu.com/p/37562283
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美团:[https://cloud.tencent.com/developer/article/1868117](https://link.zhihu.com/?target=https%3A//cloud.tencent.com/developer/article/1868117)
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美团:[https://cloud.tencent.com/developer/article/1868117](https://cloud.tencent.com/developer/article/1868117)
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Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate (SIGIR'2018)
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@@ -382,9 +382,8 @@ Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model fo
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https://zhuanlan.zhihu.com/p/291406172
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爱奇艺:[https://www.6aiq.com/article/1624916831286](https://link.zhihu.com/?target=https%3A//www.6aiq.com/article/1624916831286)
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爱奇艺:[https://www.6aiq.com/article/1624916831286](https://www.6aiq.com/article/1624916831286)
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美团:[https://mp.weixin.qq.com/s/WBwvfqOTDKCwGgoaGoSs6Q](https://link.zhihu.com/?target=https%3A//mp.weixin.qq.com/s/WBwvfqOTDKCwGgoaGoSs6Q)
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多任务loss优化:[https://blog.csdn.net/wuzhongqi](https://link.zhihu.com/?target=https%3A//blog.csdn.net/wuzhongqiang/article/details/124258128)
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美团:[https://mp.weixin.qq.com/s/WBwvfqOTDKCwGgoaGoSs6Q](https://mp.weixin.qq.com/s/WBwvfqOTDKCwGgoaGoSs6Q)
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多任务loss优化:[https://blog.csdn.net/wuzhongqi](https://blog.csdn.net/wuzhongqi)
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