News recommender systems are devised to alleviate the information overload, attracting more and more researchers' attention in recent years. The lack of a dedicated learner-oriented news recommendation toolkit hinders the advancement of research in news recommendation. We propose a PyTorch-based news recommendation toolkit called NewsTorch, developed to support learners in acquiring both conceptual understanding and practical experience. This toolkit provides a modular, decoupled, and extensible framework with a learner-friendly GUI platform that supports dataset downloading and preprocessing. It also enables training, validation, and testing of state-of-the-art neural news recommendation models with standardized evaluation metrics, ensuring fair comparison and reproducible experiments. Our open-source toolkit is released on Github: https://github.com/whonor/NewsTorch.

翻译：新闻推荐系统旨在缓解信息过载问题，近年来吸引了越来越多研究者的关注。当前缺乏专门面向学习者的新闻推荐工具包，这阻碍了新闻推荐研究的发展。本文提出一个基于PyTorch的新闻推荐工具包NewsTorch，旨在帮助学习者同时掌握概念理解和实践技能。该工具包提供模块化、解耦且可扩展的框架，并配备支持数据集下载与预处理的学习者友好型图形用户界面。同时，它能够使用标准化评估指标对前沿神经新闻推荐模型进行训练、验证和测试，确保公平对比和实验可复现。我们的开源工具包已发布在Github上：https://github.com/whonor/NewsTorch。