# Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networks

## TL;DR

This paper introduces a hierarchical RNN model that personalizes session-based recommendations by transferring information across user sessions, significantly improving performance in real-world datasets.

## Contribution

The paper proposes a novel hierarchical RNN architecture that incorporates cross-session information transfer for personalized session-based recommendations.

## Key findings

- Large improvements over session-only RNNs on industry datasets
- Effective personalization in domains with available user profiles
- Demonstrates the benefit of hierarchical modeling for session-based tasks

## Abstract

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.