A Variational Recurrent Neural Network for Session-Based Recommendations using Bayesian Personalized Ranking.
Panayiotis Christodoulou, Sotirios Chatzis, Andreas S. Andreou · Journal of the Association for Information Systems · 2017
This work introduces VRNN-BPR, a novel deep learning model, which is utilized in session-based Recommender systems tackling the data sparsity problem. The proposed model combines a Recurrent Neural Network with an amortized variational inference setup (AVI) and a Bayesian Personalized Ranking in order to produce predictions on sequence-based data and generate recommendations. The model is assessed using a large real-world dataset and the results demonstrate its superiority over current state-of-the-art techniques.