Predicting user responses, such as clicks and conversions, is of great
importance and has found its usage in many Web applications including
recommender systems, web search and online advertising. The data in those
applications is mostly categorical and contains multiple fields; a typical
representation is to transform it into a high-dimensional sparse binary feature
representation via one-hot encoding. Facing with the extreme sparsity,
traditional models may limit their capacity of mining shallow patterns from the
data, i.e. low-order feature combinations. Deep models like deep neural
networks, on the other hand, cannot be directly applied for the
high-dimensional input because of the huge feature space. In this paper, we
propose a Product-based Neural Networks (PNN) with an embedding layer to learn
a distributed representation of the categorical data, a product layer to
capture interactive patterns between inter-field categories, and further fully
connected layers to explore high-order feature interactions. Our experimental
results on two large-scale real-world ad click datasets demonstrate that PNNs
consistently outperform the state-of-the-art models on various metrics.

本文提出了一种基于产品的神经网络模型（PNN），通过嵌入层来学习分类数据的分布式表示，通过乘积层来捕获领域之间的互动模式，并进一步完全连接的层来探索高阶特征交互，实验证明在两个大规模实际广告点击数据集上，PNN 模型在各种指标上都优于现有的基准模型。