Product-based Neural Networks for User Response Prediction
This addresses the challenge of handling sparse categorical data in web applications such as recommender systems and online advertising, offering an incremental improvement over existing methods.
The paper tackles the problem of predicting user responses like clicks and conversions from high-dimensional sparse categorical data by proposing Product-based Neural Networks (PNN), which consistently outperform state-of-the-art models on large-scale ad click datasets.
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.