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.

特征交互选择是商业推荐系统中的一个基本问题。本文提出了一种新颖的 Cognitive EvoLutionary Learning (CELL) 框架，该框架受到自然有机体的进化和功能启发，通过进化选择适应性操作、特征和交互，从而实现对不同任务和数据的自适应演化，显著优于现有基准模型。

认知进化学习用于推荐系统的特征交互选择

Cognitive Evolutionary Learning to Select Feature Interactions for  Recommender Systems

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。