| Autor: |
Quadrana, Massimo, Karatzoglou, Alexandros, Hidasi, Balázs, Cremonesi, Paolo |
| Rok vydání: |
2017 |
| Předmět: |
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| Druh dokumentu: |
Working Paper |
| DOI: |
10.1145/3109859.3109896 |
| Popis: |
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. |
| Databáze: |
arXiv |
| Externí odkaz: |
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