  High-order interactive features capture the correlation between different
columns and thus are promising to enhance various learning tasks on ubiquitous
tabular data. To automate the generation of interactive features, existing
works either explicitly traverse the 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 search efficiency. 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. Specifically, we first present our theoretical
evidence that motivates us to search for useful interactive features with
increasing order. Then we instantiate this search strategy by optimizing both a
dedicated graph neural network (GNN) and the adjacency tensor associated with
the defined feature graph. In this way, the proposed FIVES method simplifies
the time-consuming traversal as a typical training course of GNN and enables
explicit feature generation according to the learned adjacency tensor.
Experimental results on both benchmark and real-world datasets show the
advantages of FIVES over several state-of-the-art methods. Moreover, the
interactive features identified by FIVES are deployed on the recommender system
of Taobao, a worldwide leading e-commerce platform. Results of an online A/B
testing further verify the effectiveness of the proposed method FIVES, and we
further provide FIVES as AI utilities for the customers of Alibaba Cloud.
