Learning feature interactions is the key to success for the large-scale CTR prediction and recommendation. In practice, handcrafted feature engineering usually requires exhaustive searching. In order to reduce the high cost of human efforts in feature engineering, researchers propose several deep neural networks (DNN)-based approaches to learn the feature interactions in an end-to-end fashion. However, existing methods either do not learn both vector-wise interactions and bit-wise interactions simultaneously, or fail to combine them in a controllable manner. In this paper, we propose a new model, xDeepInt, based on a novel network architecture called polynomial interaction network (PIN) which learns higher-order vector-wise interactions recursively. By integrating subspace-crossing mechanism, we enable xDeepInt to balance the mixture of vector-wise and bit-wise feature interactions at a bounded order. Based on the network architecture, we customize a combined optimization strategy to conduct feature selection and interaction selection. We implement the proposed model and evaluate the model performance on three real-world datasets. Our experiment results demonstrate the efficacy and effectiveness of xDeepInt over state-of-the-art models. We open-source the TensorFlow implementation of xDeepInt: https://github.com/yanyachen/xDeepInt.
翻译:大规模 CTR 预测和建议的成功关键在于学习特性互动。 在实践中,手工艺特征工程通常需要彻底的搜索。 为了降低人类在特征工程方面的高成本,研究人员建议采用一些基于深神经网络(DNN)的方法,以端对端的方式学习特征互动。 但是,现有的方法不是既不能同时学习矢量互动,也不能同时学习介质互动,而是不能以可控制的方式将两者结合起来。 在本文中,我们提出了一个新的模型,即xDeepInt,基于一个叫做多边互动网络(PIN)的新颖的网络结构,可以反复学习更高层次矢量与矢量之间的互动。我们通过整合子空间交叉机制,可以使xDeepIntInt在连接的顺序下平衡矢量与位特征互动的组合。基于网络结构,我们定制了一个组合优化战略,以进行特征选择和互动选择。 我们实施了拟议的模型,并评估三个真实世界数据集的模型性表现。 我们的实验结果显示xDeepFres/DeepFlent IMest 模型的效能和有效性和有效性。