Recommender Systems based on Parallel and Distributed Deep Learning
Vaios Stergiopoulos, Eleni Tousidou, Antonio Corral · 2023
As individuals have become overloaded with information, Recommender Systems (RS) were created to provide machine generated recommendations. Significant advancements in RS have been made thanks to Machine Learning methods; Deep Learning (DL) in particular has become extremely popular. Despite the fact that Deep neural networks (DNNs) upgrade notably the performance of RS, they make them larger and more memory-intensive systems. To that end, the solution is adding (data or model) parallel and distributed algorithms to DL RS. In this paper, we present our large-scale, multi-staged, hybrid RS that processes a million-scale dataset, as well as the most noteworthy parallel or/and distributed DL systems. Finally, we outline directions regarding the future evolution of our RS by adding some features and ideas from such systems.