Addressing Cold Start in Recommender Systems with Hierarchical Graph Neural Networks
This addresses the cold start issue for items in recommender systems, which is a domain-specific incremental improvement.
The paper tackled the cold start problem for new items in recommender systems by using hierarchical graph neural networks, achieving better forecasting quality than state-of-the-art methods with comparable computational time on multiple datasets.
Recommender systems have become an essential instrument in a wide range of industries to personalize the user experience. A significant issue that has captured both researchers' and industry experts' attention is the cold start problem for new items. In this work, we present a graph neural network recommender system using item hierarchy graphs and a bespoke architecture to handle the cold start case for items. The experimental study on multiple datasets and millions of users and interactions indicates that our method achieves better forecasting quality than the state-of-the-art with a comparable computational time.