# EB-NeRD: A Large-Scale Dataset for News Recommendation

arXiv: https://arxiv.org/abs/2410.03432 · HTML: https://mlsanity.com/paper/2410.03432/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 52 references that are in the map.
- First posted on arXiv 4 Oct 2024.

## Builds on (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Deep Interest Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1706.06978/) (2017)
- [Recommender Systems in the Era of Large Language Models (LLMs)](https://mlsanity.com/paper/2307.02046/) (2023)
- [NPA: Neural News Recommendation with Personalized Attention](https://mlsanity.com/paper/1907.05559/) (2019)
- [BARS: Towards Open Benchmarking for Recommender Systems](https://mlsanity.com/paper/2205.09626/) (2022)
- [Open Benchmarking for Click-Through Rate Prediction](https://mlsanity.com/paper/2009.05794/) (2020)
- [Measuring the Business Value of Recommender Systems](https://mlsanity.com/paper/1908.08328/) (2019)
- [Personalized News Recommendation: Methods and Challenges](https://mlsanity.com/paper/2106.08934/) (2021)
- [News Recommender System: A review of recent progress, challenges, and opportunities](https://mlsanity.com/paper/2009.04964/) (2020)
- [NewsBERT: Distilling Pre-trained Language Model for Intelligent News Application](https://mlsanity.com/paper/2102.04887/) (2021)
- [Recommenders with a mission: assessing diversity in newsrecommendations](https://mlsanity.com/paper/2012.10185/) (2020)
- [News Session-Based Recommendations using Deep Neural Networks](https://mlsanity.com/paper/1808.00076/) (2018)
- [Personalized News Recommendation with Knowledge-aware Interactive Matching](https://mlsanity.com/paper/2104.10083/) (2021)
- [Beyond Optimizing for Clicks: Incorporating Editorial Values in News Recommendation](https://mlsanity.com/paper/2004.09980/) (2020)
- [RADio -- Rank-Aware Divergence Metrics to Measure Normative Diversity in News Recommendations](https://mlsanity.com/paper/2209.13520/) (2022)
- [Are Big Recommendation Models Fair to Cold Users?](https://mlsanity.com/paper/2202.13607/) (2022)
- [RecSys Challenge 2024: Balancing Accuracy and Editorial Values in News Recommendations](https://mlsanity.com/paper/2409.20483/) (2024)

## Built on by (most cited first, 12 of 12)

- [Beyond Retrieval: Generating Narratives in Conversational Recommender Systems](https://mlsanity.com/paper/2410.16780/) (2024)
- [Don't Waste It: Guiding Generative Recommenders with Structured Human Priors via Multi-Head Decoding](https://mlsanity.com/paper/2511.10492/) (2025)
- [A Survey on LLM-based News Recommender Systems](https://mlsanity.com/paper/2502.09797/) (2025)
- [RecBase: Generative Foundation Model Pretraining for Zero-Shot Recommendation](https://mlsanity.com/paper/2509.03131/) (2025)
- [Efficient Sequential Recommendation for Long Term User Interest Via Personalization](https://mlsanity.com/paper/2601.03479/) (2026)
- [CollectiveKV: Decoupling and Sharing Collaborative Information in Sequential Recommendation](https://mlsanity.com/paper/2601.19178/) (2026)
- [Towards Multi-Aspect Diversification of News Recommendations Using Neuro-Symbolic AI for Individual and Societal Benefit](https://mlsanity.com/paper/2509.02220/) (2025)
- [Modeling Behavioral Patterns in News Recommendations Using Fuzzy Neural Networks](https://mlsanity.com/paper/2601.04019/) (2026)
- [NewsTorch: A PyTorch-based Toolkit for Learner-oriented News Recommendation](https://mlsanity.com/paper/2604.14510/) (2026)
- [ZoRRO: A Zero-Weight Personalized Recommender System for Scalable News Recommendation](https://mlsanity.com/paper/2607.10910/) (2026)
- [Normative Alignment of Recommender Systems via Internal Label Shift](https://mlsanity.com/paper/2607.10915/) (2026)
- [FARE: Deep Reinforcement Learning For Fair Exposure Constrained Uncertainty Aware Financial Content Personalization](https://mlsanity.com/paper/2609.31890/) (2026)

## Abstract (arXiv)

Personalized content recommendations have been pivotal to the content experience in digital media from video streaming to social networks. However, several domain specific challenges have held back adoption of recommender systems in news publishing. To address these challenges, we introduce the Ekstra Bladet News Recommendation Dataset (EB-NeRD). The dataset encompasses data from over a million unique users and more than 37 million impression logs from Ekstra Bladet. It also includes a collection of over 125,000 Danish news articles, complete with titles, abstracts, bodies, and metadata, such as categories. EB-NeRD served as the benchmark dataset for the RecSys '24 Challenge, where it was demonstrated how the dataset can be used to address both technical and normative challenges in designing effective and responsible recommender systems for news publishing. The dataset is available at: https://recsys.eb.dk.