  Feature interaction selection is a fundamental problem in commercial
recommender systems. Most approaches equally enumerate all features and
interactions by the same pre-defined operation under expert guidance. Their
recommendation is unsatisfactory sometimes due to the following issues:
(1)~They cannot ensure the learning abilities of models because their
architectures are poorly adaptable to tasks and data; (2)~Useless features and
interactions can bring unnecessary noise and complicate the training process.
In this paper, we aim to adaptively evolve the model to select appropriate
operations, features, and interactions under task guidance. Inspired by the
evolution and functioning of natural organisms, we propose a novel
\textsl{Cognitive EvoLutionary Learning (CELL)} framework, where cognitive
ability refers to a property of organisms that allows them to react and survive
in diverse environments. It consists of three stages, i.e., DNA search, genome
search, and model functioning. Specifically, if we regard the relationship
between models and tasks as the relationship between organisms and natural
environments, interactions of feature pairs can be analogous to double-stranded
DNA, of which relevant features and interactions can be analogous to genomes.
Along this line, we diagnose the fitness of the model on operations, features,
and interactions to simulate the survival rates of organisms for natural
selection. We show that CELL can adaptively evolve into different models for
different tasks and data, which enables practitioners to access off-the-shelf
models. Extensive experiments on four real-world datasets demonstrate that CELL
significantly outperforms state-of-the-art baselines. Also, we conduct
synthetic experiments to ascertain that CELL can consistently discover the
pre-defined interaction patterns for feature pairs.
