04-《DeepFM: A Factorization-Machine based Neural Network for CTR Prediction》
1.背景 DeepFM，2017年被提出，是一个以并行形式结合了DNN和FM的模型。它被提出用于解决构建复杂特征组合的问题。 在CTR预测中，学习用户点击行为背后的隐藏特征组合是非常重要的。这些隐藏特征组合通常比较复杂，包括了低阶和高阶。我们需要注意是的，低阶和高阶的特征组合是同样重要的，我们不应该忽略任意一个。 构建特征组合的问题，其实一直是CTR预测中的一个难题。 对于复杂高阶特征组合而言，多
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相关文章
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