  In this paper, we present GRecX, an open-source TensorFlow framework for
benchmarking GNN-based recommendation models in an efficient and unified way.
GRecX consists of core libraries for building GNN-based recommendation
benchmarks, as well as the implementations of popular GNN-based recommendation
models. The core libraries provide essential components for building efficient
and unified benchmarks, including FastMetrics (efficient metrics computation
libraries), VectorSearch (efficient similarity search libraries for dense
vectors), BatchEval (efficient mini-batch evaluation libraries), and
DataManager (unified dataset management libraries). Especially, to provide a
unified benchmark for the fair comparison of different complex GNN-based
recommendation models, we design a new metric GRMF-X and integrate it into the
FastMetrics component. Based on a TensorFlow GNN library tf_geometric, GRecX
carefully implements a variety of popular GNN-based recommendation models. We
carefully implement these baseline models to reproduce the performance reported
in the literature, and our implementations are usually more efficient and
friendly. In conclusion, GRecX enables uses to train and benchmark GNN-based
recommendation baselines in an efficient and unified way. We conduct
experiments with GRecX, and the experimental results show that GRecX allows us
to train and benchmark GNN-based recommendation baselines in an efficient and
unified way. The source code of GRecX is available at
https://github.com/maenzhier/GRecX.
