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

Abstract

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.

11 pages, 8 tables, 2 figures, RecSys '24

References in corpus (7)

• XGBoost: A Scalable Tree Boosting System
• Recommender Systems in the Era of Large Language Models (LLMs)
• NPA: Neural News Recommendation with Personalized Attention
• Measuring the Business Value of Recommender Systems
• News Session-Based Recommendations using Deep Neural Networks
• Beyond Optimizing for Clicks: Incorporating Editorial Values in News Recommendation
• RecSys Challenge 2024: Balancing Accuracy and Editorial Values in News Recommendations

Cited by in corpus (4)

• D-RDW: Diversity-Driven Random Walks for News Recommender Systems
• Informfully Recommenders -- Reproducibility Framework for Diversity-aware Intra-session Recommendations
• Normative Alignment of Recommender Systems via Internal Label Shift
• ZoRRO: A Zero-Weight Personalized Recommender System for Scalable News Recommendation