论文标题
通过分层图神经网络在推荐系统中解决冷启动
Addressing Cold Start in Recommender Systems with Hierarchical Graph Neural Networks
论文作者
论文摘要
推荐系统已成为广泛的行业中必不可少的工具，以个性化用户体验。引起研究人员和行业专家的关注的一个重大问题是新项目的冷启动问题。在这项工作中，我们使用项目层次结构图和定制体系结构来介绍图形神经网络推荐系统，以处理项目的冷启动箱。对多个数据集以及数百万用户和交互的实验研究表明，与具有可比的计算时间的最先进的方法相比，我们的方法的预测质量更好。
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.
