Deep sparse networks are widely investigated as a neural network architecture for prediction tasks with high-dimensional sparse features, with which feature interaction selection is a critical component. While previous methods primarily focus on how to search feature interaction in a coarse-grained space, less attention has been given to a finer granularity. In this work, we introduce a hybrid-grained feature interaction selection approach that targets both feature field and feature value for deep sparse networks. To explore such expansive space, we propose a decomposed space which is calculated on the fly. We then develop a selection algorithm called OptFeature, which efficiently selects the feature interaction from both the feature field and the feature value simultaneously. Results from experiments on three large real-world benchmark datasets demonstrate that OptFeature performs well in terms of accuracy and efficiency. Additional studies support the feasibility of our method.

翻译：深度稀疏网络作为处理高维稀疏特征的预测任务神经网络架构被广泛研究，其中特征交互选择是关键组成部分。现有方法主要关注如何在粗粒度空间中搜索特征交互，对更细粒度的关注较少。本研究提出一种面向深度稀疏网络的混合粒度特征交互选择方法，同时针对特征域和特征值进行目标优化。为探索此类广阔空间，我们提出一种可实时计算的分解空间，进而开发名为OptFeature的选择算法，该算法能同时高效地从特征域和特征值维度进行特征交互选择。在三个大型真实世界基准数据集上的实验结果表明，OptFeature在准确性和效率方面均表现优异。进一步研究验证了该方法的可行性。