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arXiv · 2205.05209

Bayesian Prior Learning via Neural Networks for Next-item Recommendation

Manoj Reddy Dareddy·Zijun Xue·Nicholas Lin·Junghoo Cho
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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.

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Manoj Reddy Dareddy, Zijun Xue, Nicholas Lin, Junghoo Cho. 2022-05-10. Bayesian Prior Learning via Neural Networks for Next-item Recommendation. https://arxiv.org/abs/2205.05209

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