Hierarchical Bayesian Personalized Recommendation: A Case Study and Beyond

TL;DR

This paper introduces HBayes, a hierarchical Bayesian framework for personalized recommendations that leverages hierarchical item structures, demonstrating improved performance over existing models on real datasets.

Contribution

The paper presents a novel hierarchical Bayesian learning framework with a fast variational inference algorithm for personalized recommendation systems.

Findings

Outperforms state-of-the-art models in precision, recall, and NDCG.

Effective learning of hierarchical structures and latent factors.

Demonstrates benefits across different real-world datasets.

Abstract

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}.