  In this paper, we apply a mini-batch based negative sampling method to
efficiently train a latent factor autoencoder model on large scale and sparse
data for implicit feedback collaborative filtering. We compare our work against
a state-of-the-art baseline model on different experimental datasets and show
that this method can lead to a good and fast approximation of the baseline
model performance. The source code is available in
https://github.com/amoussawi/recoder .
