Bayesian Prior Learning via Neural Networks for Next-item Recommendation

TL;DR

This paper introduces a Bayesian neural network approach for next-item recommendation that models user interaction sequences, outperforming existing methods and advancing privacy-preserving recommender systems.

Contribution

It presents a novel method combining neural networks with Bayesian estimation to improve next-item prediction accuracy.

Findings

Outperforms state-of-the-art baselines on real datasets

Effectively models user interaction sequences

Advances privacy-preserving recommender systems

Abstract

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.