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：深度稀疏网络作为一种面向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。以往方法主要关注如何在粗粒度空间中搜索特征交互，而对更细粒度的关注不足。本文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，该方法同时针对特征域和特征值进行建模。为探索如此庞大的空间，我们提出了一种即时计算的分解空间。随后我们开发了名为OptFeature的选择算法，该算法能同时高效地从特征域和特征值两个维度进行特征交互选择。在三个大规模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。附加研究进一步验证了我们方法的可行性。