  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.
