Towards Large Scale Training Of Autoencoders For Collaborative Filtering
This work addresses scalability issues in collaborative filtering for recommendation systems, but it is incremental as it builds on existing autoencoder methods.
The paper tackles the challenge of efficiently training latent factor autoencoders on large-scale, sparse implicit feedback data for collaborative filtering by applying a mini-batch negative sampling method, achieving a good and fast approximation of state-of-the-art baseline performance.
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 .