NewsTorch: A PyTorch-based Toolkit for Learner-oriented News Recommendation
Rongyao Wang, Veronica Liesaputra, Zhiyi Huang
TLDR
NewsTorch is a PyTorch toolkit for learner-oriented news recommendation, offering a modular framework for understanding and experimenting with models.
Key contributions
- PyTorch-based toolkit for learner-oriented news recommendation.
- Modular, decoupled, and extensible framework with a user-friendly GUI.
- Supports dataset downloading, preprocessing, training, and evaluation.
- Enables fair comparison and reproducible experiments with SOTA models.
Why it matters
This toolkit addresses the lack of dedicated learner-oriented tools in news recommendation research. It simplifies the process of understanding and experimenting with complex models. By providing a standardized platform, NewsTorch fosters reproducible research and accelerates advancements in the field.
Original 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.
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