Recurrent Neural Networks for Long and Short-Term Sequential Recommendation

Kiewan Villatel 1, 2 Elena Smirnova 1 Jérémie Mary 1 Philippe Preux 2
2 SEQUEL - Sequential Learning
Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
Abstract : Recommender systems objectives can be broadly characterized as modeling user preferences over short-or long-term time horizon. A large body of previous research studied long-term recommendation through dimensionality reduction techniques applied to the historical user-item interactions. A recently introduced session-based recommendation setting highlighted the importance of modeling short-term user preferences. In this task, Recurrent Neural Networks (RNN) have shown to be successful at capturing the nuances of user's interactions within a short time window. In this paper, we evaluate RNN-based models on both short-term and long-term recommendation tasks. Our experimental results suggest that RNNs are capable of predicting immediate as well as distant user interactions. We also find the best performing configuration to be a stacked RNN with layer normalization and tied item embeddings.
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https://hal.inria.fr/hal-01847127
Contributor : Kiewan Villatel <>
Submitted on : Monday, July 23, 2018 - 11:38:31 AM
Last modification on : Friday, April 19, 2019 - 4:55:26 PM
Long-term archiving on : Wednesday, October 24, 2018 - 2:30:27 PM

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  • HAL Id : hal-01847127, version 1
  • ARXIV : 1807.09142

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Kiewan Villatel, Elena Smirnova, Jérémie Mary, Philippe Preux. Recurrent Neural Networks for Long and Short-Term Sequential Recommendation. 2018. ⟨hal-01847127⟩

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