# Towards Large Scale Training Of Autoencoders For Collaborative Filtering

arXiv: https://arxiv.org/abs/1809.00999 · HTML: https://mlsanity.com/paper/1809.00999/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Aug 2018.

## Built on by (most cited first, 1 of 1)

- [Resource Heterogeneity-Aware and Utilization-Enhanced Scheduling for Deep Learning Clusters](https://mlsanity.com/paper/2503.10918/) (2025)

## Abstract (arXiv)

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 .