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arXiv · 2410.13125

Transformers4NewsRec: A Transformer-based News Recommendation Framework

Dairui Liu·Honghui Du·Boming Yang·Neil Hurley·Aonghus Lawlor·Irene Li·Derek Greene·Ruihai Dong
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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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Dairui Liu, Honghui Du, Boming Yang, Neil Hurley, Aonghus Lawlor, Irene Li, Derek Greene, Ruihai Dong. 2024-10-17. Transformers4NewsRec: A Transformer-based News Recommendation Framework. https://arxiv.org/abs/2410.13125

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