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.
@article{arxiv.2310.01146,
title = {NewsRecLib: A PyTorch-Lightning Library for Neural News Recommendation},
author = {Andreea Iana and Goran Glavaš and Heiko Paulheim},
journal= {arXiv preprint arXiv:2310.01146},
year = {2023}
}
Comments
Accepted at the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP 2023)