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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