# NewsRecLib: A PyTorch-Lightning Library for Neural News Recommendation

arXiv: https://arxiv.org/abs/2310.01146 · HTML: https://mlsanity.com/paper/2310.01146/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Oct 2023.

## Built on by (most cited first, 7 of 7)

- [SPAR: Personalized Content-Based Recommendation via Long Engagement Attention](https://mlsanity.com/paper/2402.10555/) (2024)
- [EmbSum: Leveraging the Summarization Capabilities of Large Language Models for Content-Based Recommendations](https://mlsanity.com/paper/2405.11441/) (2024)
- [News Without Borders: Domain Adaptation of Multilingual Sentence Embeddings for Cross-lingual News Recommendation](https://mlsanity.com/paper/2406.12634/) (2024)
- [A Survey on LLM-based News Recommender Systems](https://mlsanity.com/paper/2502.09797/) (2025)
- [Peeling Back the Layers: An In-Depth Evaluation of Encoder Architectures in Neural News Recommenders](https://mlsanity.com/paper/2410.01470/) (2024)
- [Eval4DiRec: A Unified and Systematic Evaluation Framework for Diffusion-based Recommender Systems](https://mlsanity.com/paper/2609.34404/) (2026)
- [Formal Algorithms for Model Efficiency](https://mlsanity.com/paper/2508.14000/) (2025)

## Abstract (arXiv)

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.