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.

研究了稀疏深度网络作为高维稀疏特征的预测任务的神经网络架构，并引入了一种混合粒度的特征交互选择方法，该方法同时针对特征域和特征值进行选择，通过实验结果证明该方法在准确性和效率方面表现出色。