The search for a good machine learning (ML) model takes a long time and requires the considerations of many alternatives, including data preprocessing, algorithm selection, and hyperparameter tuning methods. Thus, tedious searches face a combinatorial explosion problem. In this work, we build a new automated machine learning (AutoML) system called CF-DAML, a distributed automated system based on collaborative filtering (CF), to address these challenges by recommending and training suitable models for supervised learning tasks. CF-DAML first computes some informative meta-features for a new dataset, then uses a weighted l(1)-norm (W1-norm) to accurately calculate the k nearest neighbors (kNN) of the new dataset, and finally recommends the top N models with good performances on each of its neighbors to the new dataset. We also design a distributed system (DSTM) for training the models to reduce the time complexity substantially. In addition, we develop a multilayer selective stacked ensemble system (MSSE), whose base models are selected from among suitable candidate models based on their runtimes, classification accuracies, and diversities, to enhance the stability of CF-DAML. To our knowledge, this is the first work to combine memory-based CF and the selective stacked ensemble to solve the AutoML problem. Extensive experiments are conducted on many UCI datasets and the comparative results demonstrate that our approach outperforms the current state-of-the-art methods.