Transformers4NewsRec: A Transformer-based News Recommendation Framework
Dairui Liu, Honghui Du, Boming Yang, Neil Hurley, Aonghus Lawlor,, Irene Li, Derek Greene, Ruihai Dong

TL;DR
Transformers4NewsRec is a flexible Python framework leveraging transformer models to unify, compare, and analyze news recommendation systems, enhancing evaluation and customization capabilities.
Contribution
It introduces a comprehensive framework that integrates various news recommendation models with transformer-based architectures for improved analysis.
Findings
Facilitates comparison of different news recommendation models
Supports both deep neural networks and graph-based models
Enhances evaluation flexibility and analysis depth
Abstract
Pre-trained transformer models have shown great promise in various natural language processing tasks, including personalized news recommendations. To harness the power of these models, we introduce Transformers4NewsRec, a new Python framework built on the \textbf{Transformers} library. This framework is designed to unify and compare the performance of various news recommendation models, including deep neural networks and graph-based models. Transformers4NewsRec offers flexibility in terms of model selection, data preprocessing, and evaluation, allowing both quantitative and qualitative analysis.
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Taxonomy
TopicsRecommender Systems and Techniques · Topic Modeling
