# Hierarchical BiGraph Neural Network as Recommendation Systems

arXiv: https://arxiv.org/abs/2007.16000 · HTML: https://mlsanity.com/paper/2007.16000/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jul 2020.

## Built on by (most cited first, 1 of 1)

- [Multi-agent Auto-Bidding with Latent Graph Diffusion Models](https://mlsanity.com/paper/2503.05805/) (2025)

## Abstract (arXiv)

Graph neural networks emerge as a promising modeling method for applications dealing with datasets that are best represented in the graph domain. In specific, developing recommendation systems often require addressing sparse structured data which often lacks the feature richness in either the user and/or item side and requires processing within the correct context for optimal performance. These datasets intuitively can be mapped to and represented as networks or graphs. In this paper, we propose the Hierarchical BiGraph Neural Network (HBGNN), a hierarchical approach of using GNNs as recommendation systems and structuring the user-item features using a bigraph framework. Our experimental results show competitive performance with current recommendation system methods and transferability.