Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networks
This work addresses the need for better personalization in recommendation systems for domains like e-commerce and video streaming where user profiles are available, though it is incremental as it builds on existing RNN methods.
The paper tackled the problem of personalizing session-based recommendations by incorporating cross-session user information, resulting in large improvements over session-only RNN models on two industry datasets.
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.