NewsTorch是面向学习者的新闻推荐开源工具包，支持从数据到模型的全流程实践。
NewsTorch: A PyTorch-based Toolkit for Learner-oriented News Recommendation
- 基于PyTorch构建模块化框架，提供图形化界面和自动数据处理功能。
- 集成主流神经推荐模型，支持标准化评估与可复现实验。
- 适合初学者快速上手新闻推荐系统开发与研究。
新闻推荐系统旨在缓解信息过载问题，近年来受到越来越多研究者关注。然而，缺乏专门面向学习者的新闻推荐工具包制约了该领域研究的推进。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理论概念与实践技能。该工具包提供模块化、解耦且可扩展的架构，配备友好的图形用户界面，支持数据集下载与预处理。同时，它能训练、验证和测试前沿神经新闻推荐模型，并采用标准化评估指标，确保公平比较与实验可复现性。该开源工具包已发布于GitHub：https://github.com/whonor/NewsTorch。
原文摘要 · Abstract (English)
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.
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