Session-based recommendations are highly relevant in many modern on-line services (e.g. e-commerce, video streaming) and recommendation settings. Recently, Recurrent Neural Networks have been shown to perform very well in session-based settings. While in many session-based recommendation domains user identifiers are hard to come by, there are also domains in which user profiles are readily available. We propose a seamless way to personalize RNN models with cross-session information transfer and devise a Hierarchical RNN model that relays end evolves latent hidden states of the RNNs across user sessions. Results on two industry datasets show large improvements over the session-only RNNs.
@article{arxiv.1706.04148,
title = {Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networks},
author = {Massimo Quadrana and Alexandros Karatzoglou and Balázs Hidasi and Paolo Cremonesi},
journal= {arXiv preprint arXiv:1706.04148},
year = {2017}
}