Welcome to RS_c, the central platform for the RecSys community. We provide curated lists of recommender-systems datasets, algorithms, books, conferences and many resources more. Maybe most importantly, we publish the latest recommender-system news. If you want your news to be reported on RS_c, read here.
‘AutoGL’, a new AutoML framework for Graphs by Tsinghua University
January 4, 2021
‘Graphs’ and linked data are highly useful in generating effective recommender systems. Also in machine learning, graphs have gained popularity.
Now, researchers from Tsinghua University (China) have released AutoGL (GitHub), the first AutoML tool for graph datasets and tasks. This tool could greatly advance the ease of working with graph data and algorithms for both machine learning and recommender systems.
AutoGL (i.e. Auto Graph Learning) is an automatic machine learning (AutoML) toolkit specified for graph datasets & tasks.
It will automatically handle all the stages involved in graph learning problems, including dataset download & management, data preprocessing and feature engineering, model selection and training, hyper-parameter tuning and ensemble, which will reduce human labors and biases in the machine learning loop by a large scale. This toolkit also serves as a platform for users to implement and test their own auto or graph learning methods. The workflow below gives the overall framework of AutoGL.