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

TL;DR

This paper introduces EB-NeRD, a large-scale Danish news recommendation dataset with over 37 million impression logs and 125,000 articles, designed to advance research in personalized news recommender systems.

Contribution

The paper presents a comprehensive dataset for news recommendation, addressing domain-specific challenges and serving as a benchmark for responsible recommender system development.

Findings

EB-NeRD enables research on technical and normative challenges.

The dataset was used as the benchmark in RecSys '24 Challenge.

It facilitates the development of effective and responsible news recommender systems.

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.