DeepAI AI Chat
Log In Sign Up

Hierarchical BiGraph Neural Network as Recommendation Systems

by   Dom Huh, et al.
George Mason University

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.


Graph Learning Augmented Heterogeneous Graph Neural Network for Social Recommendation

Social recommendation based on social network has achieved great success...

Improving Sequential Recommendation with Attribute-augmented Graph Neural Networks

Many practical recommender systems provide item recommendation for diffe...

Federated Social Recommendation with Graph Neural Network

Recommender systems have become prosperous nowadays, designed to predict...

Structured Hierarchical Dialogue Policy with Graph Neural Networks

Dialogue policy training for composite tasks, such as restaurant reserva...

SUGER: A Subgraph-based Graph Convolutional Network Method for Bundle Recommendation

Bundle recommendation is an emerging research direction in the recommend...

Federated Recommendation with Additive Personalization

With rising concerns about privacy, developing recommendation systems in...

Integrating User and Item Reviews in Deep Cooperative Neural Networks for Movie Recommendation

User evaluations include a significant quantity of information across on...