MassiveClicks: A Massively-Parallel Framework for Efficient Click Models Training

Click logs collect user interaction with information retrieval systems (e.g., search engines). Clicks therefore become implicit feedback for such systems, and are further used to train click models, which in turn improve the quality of search and recommendations results. Click models based on expectation maximization (EM) are known to be effective and robust against various biases.

Training EM-based models is challenging due to the size of click logs, and can take many hours when using sequential tools like PyClick. Alternatives, such as ParClick, employ parallelism and show significant speed-up. However, ParClick only works on single-node multi-core systems. To further scale up and out, in this work we introduce MassiveClicks, the first massively parallel, distributed, multi-GPU framework for EM-based click-models training. MassiveClicks relies on efficient GPU kernels, balanced data-partitioning policies, and distributed computing to improve the performance of EM-based model training, outperforming ParClick by orders of magnitude when using GPUs and/or multiple nodes. Additionally, the framework supports heterogeneous GPU architectures, variable numbers of GPUs per node, allows for multi-node multi-core CPU-based training when no GPUs are available.