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

Panayiotis Christodoulou (), Sotirios P. Chatzis () and Andreas S. Andreou ()
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Panayiotis Christodoulou: Cyprus University of Technology
Sotirios P. Chatzis: Cyprus University of Technology
Andreas S. Andreou: Cyprus University of Technology

A chapter in Advances in Information Systems Development, 2018, pp 51-64 from Springer

Abstract: 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 (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnichp:978-3-319-74817-7_4

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DOI: 10.1007/978-3-319-74817-7_4

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