基于Transformer的新闻推荐框架，统一对比多种模型性能。
Transformers4NewsRec: A Transformer-based News Recommendation Framework
- 基于Hugging Face Transformers构建，支持多类推荐模型
- 可灵活选择模型、预处理和评估方式，支持定量与定性分析
- 适合研究者快速验证新闻推荐算法效果
预训练Transformer模型在自然语言处理任务中表现出色，包括个性化新闻推荐。为充分利用这些模型的能力，我们提出了Transformers4NewsRec，一个基于Python的新型框架，建立在Transformers库之上。该框架旨在统一并比较各类新闻推荐模型的性能，涵盖深度神经网络和图模型。Transformers4NewsRec在模型选择、数据预处理和评估方面提供灵活性，支持定量与定性分析，便于研究者高效开展实验与对比。
原文摘要 · Abstract (English)
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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