NewsRecLib: A PyTorch-Lightning Library for Neural News Recommendation

TL;DR

NewsRecLib is an open-source, modular PyTorch-Lightning library designed to facilitate reproducible research and comprehensive evaluation of neural news recommendation models with extensive configurability and built-in models.

Contribution

It introduces a unified, configurable framework that simplifies experimental setup and analysis for neural news recommendation research.

Findings

Provides out-of-the-box implementations of neural models

Enables thorough analysis of model components and training regimes

Supports standard benchmarks and metrics

Abstract

NewsRecLib is an open-source library based on Pytorch-Lightning and Hydra developed for training and evaluating neural news recommendation models. The foremost goals of NewsRecLib are to promote reproducible research and rigorous experimental evaluation by (i) providing a unified and highly configurable framework for exhaustive experimental studies and (ii) enabling a thorough analysis of the performance contribution of different model architecture components and training regimes. NewsRecLib is highly modular, allows specifying experiments in a single configuration file, and includes extensive logging facilities. Moreover, NewsRecLib provides out-of-the-box implementations of several prominent neural models, training methods, standard evaluation benchmarks, and evaluation metrics for news recommendation.