Year: 2025
Type: conference-paper
Abstract: In the evolving realm of recommender systems, our study contributes to the understanding of potential improvements in news recommendation beyond accuracy.Central to our research is the integration of insights from news industry experts and prospective readers, compared with automated news recommendations.We conducted a labeling study with 168 articles, using Best-Worst Scaling (BWS) for ranking and topic modeling.This approach enabled a thorough examination of stakeholder expectations for ideal reading recommendations, specifically by investigating the gap between stated and revealed preferences.Our findings show alignment in ranking behavior among journalists, prospective readers, and the BM-25 algorithm.However, preferences for different beyondaccuracy measures varied.Accompanying this work, a corpus of news articles and the labeled rankings have been made available.
Institution: TU Wien
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4411278019