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

In: Advances in Information Systems Development

Author

Listed:
  • Panayiotis Christodoulou

    (Cyprus University of Technology)

  • Sotirios P. Chatzis

    (Cyprus University of Technology)

  • Andreas S. Andreou

    (Cyprus University of Technology)

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.

Suggested Citation

  • Panayiotis Christodoulou & Sotirios P. Chatzis & Andreas S. Andreou, 2018. "A Variational Latent Variable Model with Recurrent Temporal Dependencies for Session-Based Recommendation (VLaReT)," Lecture Notes in Information Systems and Organization, in: Nearchos Paspallis & Marios Raspopoulos & Chris Barry & Michael Lang & Henry Linger & Christoph Schn (ed.), Advances in Information Systems Development, pages 51-64, Springer.
    • Handle: RePEc:spr:lnichp:978-3-319-74817-7_4
    DOI: 10.1007/978-3-319-74817-7_4
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