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

TL;DR

NewsTorch is an open-source, PyTorch-based toolkit designed to facilitate research and development in learner-oriented news recommendation systems through modular design, user-friendly interface, and standardized evaluation.

Contribution

It introduces a comprehensive, extensible toolkit supporting dataset handling, model training, and evaluation, promoting reproducibility and fair comparison in news recommendation research.

Findings

Supports training and testing of state-of-the-art neural models

Provides a user-friendly GUI for dataset management and experimentation

Ensures reproducibility with standardized metrics

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.