  Next-item prediction is a a popular problem in the recommender systems
domain. As the name suggests, the task is to recommend subsequent items that a
user would be interested in given contextual information and historical
interaction data. In our paper, we model a general notion of context via a
sequence of item interactions. We model the next item prediction problem using
the Bayesian framework and capture the probability of appearance of a sequence
through the posterior mean of the Beta distribution. We train two neural
networks to accurately predict the alpha & beta parameter values of the Beta
distribution. Our novel approach of combining black-box style neural networks,
known to be suitable for function approximation with Bayesian estimation
methods have resulted in an innovative method that outperforms various
state-of-the-art baselines. We demonstrate the effectiveness of our method in
two real world datasets. Our framework is an important step towards the goal of
building privacy preserving recommender systems.
