NewsTorch: A PyTorch-based Toolkit for Learner-oriented News Recommendation

2026-04-15

AI Summary

Existing research in news recommendation lacks a dedicated toolkit tailored for learners, hindering both educational efforts and reproducible research. To address this gap, this work proposes NewsTorch—a modular, decoupled, and extensible PyTorch-based news recommendation toolkit explicitly designed for learners. NewsTorch features an integrated graphical user interface and supports end-to-end workflows, including automatic data downloading and preprocessing, model training, validation, testing, and standardized evaluation. By implementing plug-and-play versions of mainstream neural news recommendation models, the toolkit significantly enhances experimental efficiency and facilitates fair model comparisons. The open-source release of NewsTorch aims to foster reproducibility and accelerate community-driven advancements in the field.

Technology Category

• Machine Learning: Multimodal Learning
• Natural Language Processing: (Large) Language Models
• Data Mining & Knowledge Management: Recommender Systems

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

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.

Problem

• news recommender systems
• learner-oriented toolkit
• information overload
• PyTorch-based framework
• reproducible experiments

Innovation

• NewsTorch
• learner-oriented
• modular framework
• reproducible experiments
• news recommendation