A variational latent variable model with recurrent temporal dependencies for session-based recommendation (VLaReT)
Date Issued
March 28, 2018
DOI
10.1007/978-3-319-74817-7_4
https://doi.org/10.1007/978-3-319-74817-7_4
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.