NewsTorch: A PyTorch-based Toolkit for Learner-oriented News Recommendation
This toolkit addresses the lack of a dedicated learner-oriented news recommendation toolkit, enabling easier access and experimentation for students and researchers new to the field.
The authors present NewsTorch, a PyTorch-based toolkit for news recommendation that provides a modular framework and GUI to support learners in understanding and experimenting with state-of-the-art models. The toolkit includes dataset preprocessing, training, and standardized evaluation for reproducibility.
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.