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.
@article{arxiv.2410.13125,
title = {Transformers4NewsRec: A Transformer-based News Recommendation Framework},
author = {Dairui Liu and Honghui Du and Boming Yang and Neil Hurley and Aonghus Lawlor and Irene Li and Derek Greene and Ruihai Dong},
journal= {arXiv preprint arXiv:2410.13125},
year = {2024}
}