Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/12008
Title: A variational latent variable model with recurrent temporal dependencies for session-based recommendation (VLaReT)
Authors: Christodoulou, Panayiotis 
Chatzis, Sotirios P. 
Andreou, Andreas S. 
Major Field of Science: Engineering and Technology
Field Category: Computer and Information Sciences;Electrical Engineering - Electronic Engineering - Information Engineering
Keywords: Deep learning recommender systems;Latent variable models;Recurrent networks
Issue Date: 28-Mar-2018
Source: Advances in Information Systems Development. Lecture Notes in Information Systems and Organisation, vol 26, 2018, pp. 51-64
DOI: https://doi.org/10.1007/978-3-319-74817-7_4
Project: DOSSIER-CLOUD - Devops-Based Software Engineering for the Cloud 
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.
Description: Lecture Notes in Information Systems and Organisation, Volume 26
ISBN: 978-3-319-74817-7 (online)
978-3-319-74816-0 (print)
DOI: 10.1007/978-3-319-74817-7_4
Rights: © Springer
Type: Book Chapter
Affiliation : Cyprus University of Technology 
Publication Type: Peer Reviewed
Appears in Collections:Κεφάλαια βιβλίων/Book chapters

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