Deep recommender systems (DRS) are critical for current commercial online service providers, which address the issue of information overload by recommending items that are tailored to the user's interests and preferences. They have unprecedented feature representations effectiveness and the capacity of modeling the non-linear relationships between users and items. Despite their advancements, DRS models, like other deep learning models, employ sophisticated neural network architectures and other vital components that are typically designed and tuned by human experts. This article will give a comprehensive summary of automated machine learning (AutoML) for developing DRS models. We first provide an overview of AutoML for DRS models and the related techniques. Then we discuss the state-of-the-art AutoML approaches that automate the feature selection, feature embeddings, feature interactions, and model training in DRS. We point out that the existing AutoML-based recommender systems are developing to a multi-component joint search with abstract search space and efficient search algorithm. Finally, we discuss appealing research directions and summarize the survey.

翻译：深度推荐系统（Deep Recommender Systems, DRS）是现代商业在线服务提供商的关键技术，通过推荐符合用户兴趣与偏好的项目来解决信息过载问题。该类系统在特征表示的有效性以及建模用户与项目间非线性关系方面展现出前所未有的能力。尽管技术不断进步，DRS模型与其他深度学习模型类似，依赖由人类专家设计与调优的复杂神经网络架构及其他关键组件。本文将对面向DRS模型开发的自动机器学习（AutoML）技术进行系统性综述。我们首先概述AutoML在DRS模型中的应用及相关技术，继而探讨当前最先进的AutoML方法——这些方法能够自动化DRS中的特征选择、特征嵌入、特征交互及模型训练过程。我们指出现有的基于AutoML的推荐系统正朝着具备抽象搜索空间与高效搜索算法的多组件联合搜索方向发展。最后，我们讨论具有前景的研究方向并对本综述进行总结。