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Recurrent Neural Networks for Long and Short-Term Sequential Recommendation

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Kiewan Villatel
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Elena Smirnova
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Jérémie Mary
Philippe Preux

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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Dates and versions

hal-01847127 , version 1 (23-07-2018)

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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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