To automate the generation of interactive features, recent methods are proposed to either explicitly traverse the interactive feature space or implicitly express the interactions via intermediate activations of some designed models. These two kinds of methods show that there is essentially a trade-off between feature interpretability and efficient search. To possess both of their merits, we propose a novel method named Feature Interaction Via Edge Search (FIVES), which formulates the task of interactive feature generation as searching for edges on the defined feature graph. We first present our theoretical evidence that motivates us to search for interactive features in an inductive manner. Then we instantiate this search strategy by alternatively updating the edge structure and the predictive model of a graph neural network (GNN) associated with the defined feature graph. In this way, the proposed FIVES method traverses a trimmed search space and enables explicit feature generation according to the learned adjacency tensor of the GNN. Experimental results on both benchmark and real-world datasets demonstrate the advantages of FIVES over several state-of-the-art methods.

提出一种名为 FIVES 的算法，利用图神经网络，通过在定义的特征图上搜索边来生成交互特征，将特征生成的时空复杂度降至 GNN 的训练课程，可以应用在推荐系统等领域，并在 Taobao 平台进行 A/B 测试和 Alibaba Cloud 的客户提供 AI 工具。

FIVES: 基于边缘搜索的特征交互方法用于大规模表格数据