A Variational Latent Variable Model with Recurrent Temporal Dependencies for Session-Based Recommendation (VLaReT)

  • Panayiotis Christodoulou
  • Sotirios P. Chatzis
  • Andreas S. Andreou
Conference paper
Part of the Lecture Notes in Information Systems and Organisation book series (LNISO, volume 26)

Abstract

This paper presents an innovative deep learning model, namely the Variational Latent Variable Model with Recurrent Temporal Dependencies for Session-Based Recommendation (VLaReT). Our method combines a Recurrent Neural Network with Amortized Variational Inference (AVI) to enable increased predictive learning capabilities for sequential data. We use VLaReT to build a session-based Recommender System that can effectively deal with the data sparsity problem. We posit that this capability will allow for producing more accurate recommendations on a real-world sequence-based dataset. We provide extensive experimental results which demonstrate that the proposed model outperforms currently state-of-the-art approaches.

Keywords

Recurrent networks Latent variable models Deep learning recommender systems 

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Panayiotis Christodoulou
    • 1
  • Sotirios P. Chatzis
    • 1
  • Andreas S. Andreou
    • 1
  1. 1.Cyprus University of TechnologyLimassolCyprus

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