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Title: Recurrent latent variable networks for session-based recommendation
Authors: Chatzis, Sotirios P. 
Christodoulou, Panayiotis 
Andreou, Andreas S. 
Keywords: Amortized variational inference;Data sparsity;Latent variable model;Recurrent neural network;Session-based recommendation
Category: Computer and Information Sciences
Field: Engineering and Technology
Issue Date: 27-Aug-2017
Publisher: Association for Computing Machinery
Source: DLRS 2017 Proceedings of the 2nd Workshop on Deep Learning for Recommender Systems, 2017, Como, Italy, 27 August, pp. 38-45
Conference: Workshop on Deep Learning for Recommender Systems 
Abstract: 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
ISBN: 978-1-4503-5353-3
DOI: 10.1145/3125486.3125493
Rights: © Association for Computing Machinery.
Type: Conference Papers
Appears in Collections:Δημοσιεύσεις σε συνέδρια/Conference papers

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