Journalistic media increasingly address changing user behaviour online by implementing algorithmic recommendations on their pages. While social media extensively rely on user data for personalized recommendations, journalistic media may choose to aim to improve the user experience based on textual features such as thematic similarity. From a societal viewpoint, these recommendations should be as diverse as possible. Users, however, tend to prefer recommendations that enable “serendipity”—the perception of an item as a welcome surprise that strikes just the right balance between more similarly useful but still novel content. By conducting a representative online survey with n = 588 respondents, we investigate how users evaluate algorithmic news recommendations (recommendation satisfaction, as well as perceived novelty and unexpectedness) based on different similarity settings and how individual dispositions (news interest, civic information norm, need for cognitive closure, etc.) may affect these evaluations. The core piece of our survey is a self-programmed recommendation system that accesses a database of vectorized news articles. Respondents search for a personally relevant keyword and select a suitable article, after which another article is recommended automatically, at random, using one of three similarity settings. Our findings show that users prefer recommendations of the most similar articles, which are at the same time perceived as novel, but not necessarily unexpected. However, user evaluations will differ depending on personal characteristics such as formal education, the civic information norm, and the need for cognitive closure.

中文翻译：

---

一位推荐人适合所有人？基于文本的新闻推荐系统的用户满意度探索

新闻媒体越来越多地通过在其页面上实施算法推荐来解决在线用户行为的变化。虽然社交媒体广泛依赖用户数据进行个性化推荐，但新闻媒体可能会选择基于主题相似性等文本特征来改善用户体验。从社会角度来看，这些建议应尽可能多样化。然而，用户往往更喜欢能够实现“意外发现”的推荐——将一个项目视为一个受欢迎的惊喜，在更多类似有用但仍然新颖的内容之间取得恰到好处的平衡。通过对 n = 588 名受访者进行具有代表性的在线调查，我们调查了用户如何评估算法新闻推荐（推荐满意度、以及感知的新颖性和意外性）基于不同的相似性设置以及个人倾向（新闻兴趣、公民信息规范、认知闭合的需要等）如何影响这些评估。我们调查的核心部分是一个自编程的推荐系统，它可以访问矢量化新闻文章的数据库。受访者搜索与个人相关的关键字并选择合适的文章，然后使用三种相似度设置中的一种随机自动推荐另一篇文章。我们的研究结果表明，用户更喜欢最相似文章的推荐，这些文章同时被认为是新颖的，但不一定是出乎意料的。但是，用户评价会因个人特征而有所不同，例如正规教育、公民信息规范、