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.
A prior version of this paper has been published in the ISD2017 Proceedings (http://aisel.aisnet.org/isd2014/proceedings2017).
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Christodoulou, P., Chatzis, S.P., Andreou, A.S. (2018). A Variational Latent Variable Model with Recurrent Temporal Dependencies for Session-Based Recommendation (VLaReT). In: Paspallis, N., Raspopoulos, M., Barry, C., Lang, M., Linger, H., Schneider, C. (eds) Advances in Information Systems Development. Lecture Notes in Information Systems and Organisation, vol 26. Springer, Cham. https://doi.org/10.1007/978-3-319-74817-7_4
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