Transformers4NewsRec: A Transformer-based News Recommendation Framework

Dairui Liu, Honghui Du, Boming Yang, Neil Hurley, Aonghus Lawlor, Irene Li, Derek Greene, Ruihai Dong

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.