  Modeling powerful interactions is a critical challenge in Click-through rate
(CTR) prediction, which is one of the most typical machine learning tasks in
personalized advertising and recommender systems. Although developing
hand-crafted interactions is effective for a small number of datasets, it
generally requires laborious and tedious architecture engineering for extensive
scenarios. In recent years, several neural architecture search (NAS) methods
have been proposed for designing interactions automatically. However, existing
methods only explore limited types and connections of operators for interaction
generation, leading to low generalization ability. To address these problems,
we propose a more general automated method for building powerful interactions
named AutoPI. The main contributions of this paper are as follows: AutoPI
adopts a more general search space in which the computational graph is
generalized from existing network connections, and the interactive operators in
the edges of the graph are extracted from representative hand-crafted works. It
allows searching for various powerful feature interactions to produce higher
AUC and lower Logloss in a wide variety of applications. Besides, AutoPI
utilizes a gradient-based search strategy for exploration with a significantly
low computational cost. Experimentally, we evaluate AutoPI on a diverse suite
of benchmark datasets, demonstrating the generalizability and efficiency of
AutoPI over hand-crafted architectures and state-of-the-art NAS algorithms.
