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.

翻译：在许多行业中,建议系统已成为使用户经历个性化的基本工具,一个吸引研究人员和行业专家注意的重要问题是新项目冷启动问题。在这项工作中,我们提出了一个图形神经网络建议系统,使用项目等级图和直言结构处理项目冷启动案例。关于多个数据集和数百万用户和互动的实验研究表明,我们的方法比最先进的、具有可比计算时间的系统更能预测质量。