Deep & Cross Network总结

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Deep & Cross Network总结

一.介绍 CTR预估全称是Click Through Rate,就是展示给用户的广告或者商品,估计用户点击的概率.公司规模较大的时候,CTR直接影响的价值在数十亿美元的级别.广告支付一个非常流行的模型就是CPC(cost-per-click),就是按照用户的点击来付钱.那么准确的进行CTR预估,展现给用户他们最可能点击的广告就非常重要了. 传统的CTR预估模型需要大量的特征工程,耗时耗力:引入DNN之后,依靠神经网络强大的学习能力,可以一定程度上实现自动学习特征组合.但是DNN的缺点在于隐式的学…

【Paper】Deep & Cross Network for Ad Click Predictions

目录背景相关工作主要贡献核心思想 Embedding和Stacking层交叉网络(Cross Network) 深度网络(Deep Network) 组合层(Combination Layer) 理论分析多项式近似 FM的泛化高效映射总结及思考背景探索具有预测能力的组合特征对提高CTR模型的性能十分重要,这也是大量人工特征工程存在的原因.但是数据高维稀疏(大量离散特征one-hot之后)的性质,对特征探索带来了巨大挑战,进而限制了许多大型系统只能使用线性模型(比如逻辑回归).…

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