# Beyond Optimizing for Clicks: Incorporating Editorial Values in News Recommendation

arXiv: https://arxiv.org/abs/2004.09980 · HTML: https://mlsanity.com/paper/2004.09980/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Apr 2020.

## Built on by (most cited first, 8 of 8)

- [What are you optimizing for? Aligning Recommender Systems with Human Values](https://mlsanity.com/paper/2107.10939/) (2021)
- [EB-NeRD: A Large-Scale Dataset for News Recommendation](https://mlsanity.com/paper/2410.03432/) (2024)
- [RADio -- Rank-Aware Divergence Metrics to Measure Normative Diversity in News Recommendations](https://mlsanity.com/paper/2209.13520/) (2022)
- [RecSys Challenge 2024: Balancing Accuracy and Editorial Values in News Recommendations](https://mlsanity.com/paper/2409.20483/) (2024)
- [Recommender Systems for Good (RS4Good): Survey of Use Cases and a Call to Action for Research that Matters](https://mlsanity.com/paper/2411.16645/) (2024)
- [Positive AI: Key Challenges in Designing Artificial Intelligence for Wellbeing](https://mlsanity.com/paper/2304.12241/) (2023)
- [Controlled Personalization in Legacy Media Online Services: A Case Study in News Recommendation](https://mlsanity.com/paper/2510.09136/) (2025)
- [Normative Alignment of Recommender Systems via Internal Label Shift](https://mlsanity.com/paper/2607.10915/) (2026)

## Abstract (arXiv)

With the uptake of algorithmic personalization in the news domain, news organizations increasingly trust automated systems with previously considered editorial responsibilities, e.g., prioritizing news to readers. In this paper we study an automated news recommender system in the context of a news organization's editorial values. We conduct and present two online studies with a news recommender system, which span one and a half months and involve over 1,200 users. In our first study we explore how our news recommender steers reading behavior in the context of editorial values such as serendipity, dynamism, diversity, and coverage. Next, we present an intervention study where we extend our news recommender to steer our readers to more dynamic reading behavior. We find that (i) our recommender system yields more diverse reading behavior and yields a higher coverage of articles compared to non-personalized editorial rankings, and (ii) we can successfully incorporate dynamism in our recommender system as a re-ranking method, effectively steering our readers to more dynamic articles without hurting our recommender system's accuracy.