  Automated Machine Learning (AutoML) techniques have recently been introduced
to design Collaborative Filtering (CF) models in a data-specific manner.
However, existing works either search architectures or hyperparameters while
ignoring the fact they are intrinsically related and should be considered
together. This motivates us to consider a joint hyperparameter and architecture
search method to design CF models. However, this is not easy because of the
large search space and high evaluation cost. To solve these challenges, we
reduce the space by screening out usefulness yperparameter choices through a
comprehensive understanding of individual hyperparameters. Next, we propose a
two-stage search algorithm to find proper configurations from the reduced
space. In the first stage, we leverage knowledge from subsampled datasets to
reduce evaluation costs; in the second stage, we efficiently fine-tune top
candidate models on the whole dataset. Extensive experiments on real-world
datasets show better performance can be achieved compared with both
hand-designed and previous searched models. Besides, ablation and case studies
demonstrate the effectiveness of our search framework.
