Recommender systems play a significant role in information filtering and have been utilized in different scenarios, such as e-commerce and social media. With the prosperity of deep learning, deep recommender systems show superior performance by capturing non-linear information and item-user relationships. However, the design of deep recommender systems heavily relies on human experiences and expert knowledge. To tackle this problem, Automated Machine Learning (AutoML) is introduced to automatically search for the proper candidates for different parts of deep recommender systems. This survey performs a comprehensive review of the literature in this field. Firstly, we propose an abstract concept for AutoML for deep recommender systems (AutoRecSys) that describes its building blocks and distinguishes it from conventional AutoML techniques and recommender systems. Secondly, we present a taxonomy as a classification framework containing feature selection search, embedding dimension search, feature interaction search, model architecture search, and other components search. Furthermore, we put a particular emphasis on the search space and search strategy, as they are the common thread to connect all methods within each category and enable practitioners to analyze and compare various approaches. Finally, we propose four future promising research directions that will lead this line of research.

翻译：推荐人系统在信息过滤方面起着重要作用,并且在电子商务和社交媒体等不同情景中得到了利用。随着深层学习的繁荣,深层推荐人系统通过捕捉非线性信息和项目用户关系显示出优异的性能。然而,深层推荐人系统的设计在很大程度上依赖于人类的经验和专业知识。为解决这一问题,引入了自动机器学习(自动学习)系统,以自动搜索深层建议人系统不同部分的适当候选人。这项调查对这一领域的文献进行了全面审查。首先,我们为深层推荐人系统(AutoRecSys)提出了一个“自动ML”的抽象概念,描述其构建方块,并将其与常规的自动调配技术和推荐人系统区分开来。第二,我们提出了一个分类学分类框架,其中包含特征选择搜索、嵌入维度搜索、特征互动搜索、模型结构搜索和其他组成部分搜索。此外,我们特别强调搜索空间和搜索战略,因为它们是每一类别中所有方法的通用线索,使从业人员能够分析和比较各种方法。最后,我们提出了四个有希望的未来研究方向,将引导这一研究方针。