Hierarchical BiGraph Neural Network as Recommendation Systems
This work addresses recommendation systems for users and items with sparse data, but it is incremental as it builds on existing GNN methods.
The paper tackles the problem of sparse and feature-poor data in recommendation systems by proposing the Hierarchical BiGraph Neural Network (HBGNN), which uses a hierarchical GNN approach within a bigraph framework, achieving competitive performance and transferability.
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.