The paper “NewsRecLib: A PyTorch-Lightning Library for Neural News Recommendation” by Andreea Iana, Goran Glavaš, and Heiko Paulheim has been accepted at the 2023 Conference on Empirical Methods in Natural Language Processing, System Demonstrations Track.
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.