mlsanityData to 7 Oct 2026

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

arXiv 2410.03432 ↗First posted 4 Oct 2024cs.IR cs.AI cs.LGcited by 12

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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.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 18 of its 52 references that are in the map. First posted on arXiv 4 Oct 2024.

Builds on

18 of 52 references.

References · 18

  1. 2017Attention Is All You Needcited by 59,932
  2. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,120
  3. 2017Deep Interest Network for Click-Through Rate Predictioncited by 541
  4. 2023Recommender Systems in the Era of Large Language Models (LLMs)cited by 188
  5. 2019NPA: Neural News Recommendation with Personalized Attentioncited by 73
  6. 2022BARS: Towards Open Benchmarking for Recommender Systemscited by 54
  7. 2020Open Benchmarking for Click-Through Rate Predictioncited by 53
  8. 2019Measuring the Business Value of Recommender Systemscited by 32
  9. 2021Personalized News Recommendation: Methods and Challengescited by 30
  10. 2020News Recommender System: A review of recent progress, challenges, and opportunitiescited by 24
  11. 2021NewsBERT: Distilling Pre-trained Language Model for Intelligent News Applicationcited by 17
  12. 2020Recommenders with a mission: assessing diversity in newsrecommendationscited by 15
6 more
  1. 2018News Session-Based Recommendations using Deep Neural Networkscited by 12
  2. 2021Personalized News Recommendation with Knowledge-aware Interactive Matchingcited by 12
  3. 2020Beyond Optimizing for Clicks: Incorporating Editorial Values in News Recommendationcited by 8
  4. 2022RADio -- Rank-Aware Divergence Metrics to Measure Normative Diversity in News Recommendationscited by 7
  5. 2022Are Big Recommendation Models Fair to Cold Users?cited by 4
  6. 2024RecSys Challenge 2024: Balancing Accuracy and Editorial Values in News Recommendationscited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 4; 2026: 7.

12 papers.

  1. 2024Beyond Retrieval: Generating Narratives in Conversational Recommender Systemscited by 8
  2. 2025Don't Waste It: Guiding Generative Recommenders with Structured Human Priors via Multi-Head Decodingcited by 3
  3. 2025RecBase: Generative Foundation Model Pretraining for Zero-Shot Recommendationcited by 2
  4. 2025A Survey on LLM-based News Recommender Systemscited by 1
  5. 2026Efficient Sequential Recommendation for Long Term User Interest Via Personalizationcited by 1
  6. 2026CollectiveKV: Decoupling and Sharing Collaborative Information in Sequential Recommendationcited by 1
  7. 2025Towards Multi-Aspect Diversification of News Recommendations Using Neuro-Symbolic AI for Individual and Societal Benefitcited by 0
  8. 2026Modeling Behavioral Patterns in News Recommendations Using Fuzzy Neural Networkscited by 0
  9. 2026NewsTorch: A PyTorch-based Toolkit for Learner-oriented News Recommendationcited by 0
  10. 2026ZoRRO: A Zero-Weight Personalized Recommender System for Scalable News Recommendationcited by 0
  11. 2026Normative Alignment of Recommender Systems via Internal Label Shiftcited by 0
  12. 2026FARE: Deep Reinforcement Learning For Fair Exposure Constrained Uncertainty Aware Financial Content Personalizationcited by 0