1 citations · 1 across the 2 of their papers we have counts for
5 papers
Normative Alignment of Recommender Systems via Internal Label Shift
Johannes Kruse, Kasper Lindskow, Michael Riis Andersen +4
We introduce NAILS (Normative Alignment of Recommender Systems via Internal Label Shift), a simple and scalable method for aligning recommendation outputs with target distributions…
ZoRRO: A Zero-Weight Personalized Recommender System for Scalable News Recommendation
Johannes Kruse, Ryotaro Shimizu, Kasper Lindskow +4
We present ZoRRO (Zero-Weight Personalized Recommender System), a zero-weight, training-free framework for personalized news recommendation designed for scalable real-world deploym…
Disentangling Likes and Dislikes in Personalized Generative Explainable Recommendation
Ryotaro Shimizu, Takashi Wada, Yu Wang +9
Recent research on explainable recommendation generally frames the task as a standard text generation problem, and evaluates models simply based on the textual similarity between t…
EB-NeRD: A Large-Scale Dataset for News Recommendation
Johannes Kruse, Kasper Lindskow, Saikishore Kalloori +6
Personalized content recommendations have been pivotal to the content experience in digital media from video streaming to social networks. However, several domain specific challeng…
RecSys Challenge 2024: Balancing Accuracy and Editorial Values in News Recommendations
Johannes Kruse, Kasper Lindskow, Saikishore Kalloori +6
The RecSys Challenge 2024 aims to advance news recommendation by addressing both the technical and normative challenges inherent in designing effective and responsible recommender…