论文标题
除了优化点击：将编辑价值纳入新闻建议中
Beyond Optimizing for Clicks: Incorporating Editorial Values in News Recommendation
论文作者
论文摘要
随着新闻领域中算法个性化的吸收，新闻机构越来越多地信任自动化系统，以前被认为是编辑职责，例如将新闻优先级给读者。在本文中，我们在新闻机构的编辑价值观的背景下研究了自动新闻推荐系统。我们通过新闻推荐系统进行两项在线研究，该系统跨越一个半月，涉及1200多名用户。在我们的第一项研究中，我们探讨了新闻推荐如何在编辑价值观（例如偶然性，动态，多样性和覆盖率）的背景下读取行为。接下来，我们提出一项干预研究，在其中扩展新闻推荐人，以引导读者采取更具动态的阅读行为。我们发现（i）我们的推荐系统会产生更多样化的阅读行为，并且与非人性化编辑排名相比，文章的覆盖范围更高，并且（ii）我们可以成功地将推荐系统中的动态纳入重新排列方法，有效地将读者引导到我们的动态文章而不损害我们的推荐系统的准确性。
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.
