Hierarchical Autoencoder for Collaborative Filtering
Shubham Maheshwari, Angshul Majumdar · 2018
In recent years autoencoder based collaborative filtering for recommender systems have shown promise. In the past, several variants of the basic autoencoder based approach has been proposed - marginalized denoising autoencoder and stacked denoising autoencoder. However, these are not new developments; just applications of existing machine learning techniques on collaborative filtering. In this work we propose a fundamentally new architecture of hierarchical autoencoder. In a normal stacked denoising autoencoder, reconstruction only happens at the final layer, the intermediate layers are not directly responsible. In our proposed hierarchical model every layer reconstructs; each layer provides complimentary information. The output from all the layers are fused to yield the final result. Experiments of benchmark collaborative filtering datasets show the superiority of our technique over the state-of-art.