Hierarchical Bayesian Personalized Recommendation: A Case Study and Beyond
This work addresses personalized recommendation for users in systems with hierarchical item data, presenting an incremental improvement through a novel Bayesian method.
The authors tackled the problem of personalized recommendation by leveraging hierarchical item structures, proposing a hierarchical Bayesian framework (HBayes) that outperformed state-of-the-art models in precision, recall, and NDCG on two real-world datasets.
Items in modern recommender systems are often organized in hierarchical structures. These hierarchical structures and the data within them provide valuable information for building personalized recommendation systems. In this paper, we propose a general hierarchical Bayesian learning framework, i.e., \emph{HBayes}, to learn both the structures and associated latent factors. Furthermore, we develop a variational inference algorithm that is able to learn model parameters with fast empirical convergence rate. The proposed HBayes is evaluated on two real-world datasets from different domains. The results demonstrate the benefits of our approach on item recommendation tasks, and show that it can outperform the state-of-the-art models in terms of precision, recall, and normalized discounted cumulative gain. To encourage the reproducible results, we make our code public on a git repo: \url{https://tinyurl.com/ycruhk4t}.