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.

翻译：自动化机器学习（AutoML）技术近来被引入以数据特定的方式设计协同过滤（CF）模型。然而，现有工作要么搜索架构，要么搜索超参数，却忽视了二者本质相关、应被联合考虑的事实。这促使我们提出一种联合超参数与架构搜索的方法来设计CF模型。然而，由于搜索空间庞大且评估成本高昂，这一任务并不容易。为应对这些挑战，我们首先通过深入理解各超参数的作用，筛选出无用超参数选择以缩减空间。随后，我们提出一种两阶段搜索算法，从缩减后的空间中寻找合适的配置。第一阶段，我们利用子采样数据集的先验知识降低评估成本；第二阶段，我们在完整数据集上高效地微调候选模型中的最优者。在真实数据集上的大量实验表明，与手工设计及先前搜索的模型相比，本方法能取得更优性能。此外，消融实验与案例研究验证了本搜索框架的有效性。