Transformers4NewsRec: A Transformer-based News Recommendation Framework

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.