Beyond Optimizing for Clicks: Incorporating Editorial Values in News Recommendation
This addresses the challenge for news organizations to maintain editorial integrity while using automated personalization, though it is incremental as it builds on existing recommender methods.
The paper tackled the problem of aligning news recommendation algorithms with editorial values like serendipity and diversity, finding that their system increased article coverage and diversity compared to non-personalized rankings, and successfully incorporated dynamism without reducing accuracy.
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.