When two trends fuse: PyTorch and recommender systems

In the last few years, we have experienced the resurgence of neural networks owing to availability of large data sets, increased computational power, innovation in model building via deep learning, and, most importantly, open source software libraries that ease use for non-researchers. In 2016, the rapid rise of the TensorFlow library for building deep learning models allowed application developers to take state-of-the-art models and put them into production. Deep learning-based neural network research and application development is currently a very fast moving field. As such, in 2017 we have seen the emergence of the deep learning library PyTorch. At the same time, researchers in the field of recommendation systems continue to pioneer new ways to increase performance as the number of users and items increases.