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https://hdl.handle.net/20.500.14279/12628
Τίτλος: | Recurrent latent variable networks for session-based recommendation | Συγγραφείς: | Chatzis, Sotirios P. Christodoulou, Panayiotis Andreou, Andreas S. |
Major Field of Science: | Engineering and Technology | Field Category: | Computer and Information Sciences | Λέξεις-κλειδιά: | Amortized variational inference;Data sparsity;Latent variable model;Recurrent neural network;Session-based recommendation | Ημερομηνία Έκδοσης: | 27-Αυγ-2017 | Πηγή: | DLRS 2017 Proceedings of the 2nd Workshop on Deep Learning for Recommender Systems, 2017, Como, Italy, 27 August, pp. 38-45 | Project: | DOSSIER-CLOUD - Devops-Based Software Engineering for the Cloud | Conference: | Workshop on Deep Learning for Recommender Systems | Περίληψη: | In this work, we attempt to ameliorate the impact of data sparsity in the context of session-based recommendation. Specifically, we seek to devise a machine learning mechanism capable of extracting subtle and complex underlying temporal dynamics in the observed session data, so as to inform the recommendation algorithm. To this end, we improve upon systems that utilize deep learning techniques with recurrently connected units; we do so by adopting concepts from the field of Bayesian statistics, namely variational inference. Our proposed approach consists in treating the network recurrent units as stochastic latent variables with a prior distribution imposed over them. On this basis, we proceed to infer corresponding posteriors; these can be used for prediction and recommendation generation, in a way that accounts for the uncertainty in the available sparse training data. To allow for our approach to easily scale to large real-world datasets, we perform inference under an approximate amortized variational inference (AVI) setup, whereby the learned posteriors are parameterized via (conventional) neural networks. We perform an extensive experimental evaluation of our approach using challenging benchmark datasets, and illustrate its superiority over existing state-of-the-art techniques. | Description: | ACM-ICPSACM International Conference Proceeding Series | URI: | https://hdl.handle.net/20.500.14279/12628 | ISBN: | 978-1-4503-5353-3 | DOI: | 10.1145/3125486.3125493 | Rights: | © Association for Computing Machinery. | Type: | Conference Papers | Affiliation: | Cyprus University of Technology | Publication Type: | Peer Reviewed |
Εμφανίζεται στις συλλογές: | Δημοσιεύσεις σε συνέδρια /Conference papers or poster or presentation |
Αρχεία σε αυτό το τεκμήριο:
Αρχείο | Περιγραφή | Μέγεθος | Μορφότυπος | |
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Recurrent_Latent_Variable_Networks.pdf | 763.85 kB | Adobe PDF | Δείτε/ Ανοίξτε |
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