NewsRecLib: A PyTorch-Lightning Library for Neural News Recommendation

Andreea Iana, Goran Glavaš, Heiko Paulheim


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.
Anthology ID:
2023.emnlp-demo.26
Volume:
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
Month:
December
Year:
2023
Address:
Singapore
Editors:
Yansong Feng, Els Lefever
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
296–310
Language:
URL:
https://aclanthology.org/2023.emnlp-demo.26
DOI:
10.18653/v1/2023.emnlp-demo.26
Bibkey:
Cite (ACL):
Andreea Iana, Goran Glavaš, and Heiko Paulheim. 2023. NewsRecLib: A PyTorch-Lightning Library for Neural News Recommendation. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 296–310, Singapore. Association for Computational Linguistics.
Cite (Informal):
NewsRecLib: A PyTorch-Lightning Library for Neural News Recommendation (Iana et al., EMNLP 2023)
Copy Citation:
PDF:
https://aclanthology.org/2023.emnlp-demo.26.pdf
Video:
 https://aclanthology.org/2023.emnlp-demo.26.mp4