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
- 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)
- PDF:
- https://preview.aclanthology.org/dois-2013-emnlp/2023.emnlp-demo.26.pdf