科学过滤器泡沫的破灭：通过发现新作者来推动创新
Bursting Scientific Filter Bubbles: Boosting Innovation via Novel Author Discovery
摘要：
科学研究的孤立孤岛和日益增长的信息过载挑战限制了对文献的认识，阻碍了创新。网络策展和推荐通常优先考虑相关性，可以进一步加强这些信息“过滤气泡”。作为回应，我们描述了Bridger，一个促进学者及其工作发现的系统。我们构建了一个多方面的表示作者从他们的论文和推断作者人物角色收集的信息，并使用它来开发一种方法，定位科学家之间的共性和对比，以平衡相关性和新颖性。在与计算机科学研究人员的研究中，这种方法可以帮助用户发现被认为对产生新的研究方向有用的作者。我们还展示了一种显示作者信息的方法，提高了理解新的，不熟悉的学者的工作的能力。我们的分析表明，Bridger连接了具有不同引用概况并在不同场所发表的作者，提高了桥接不同科学社区的前景。
英文摘要：
Isolated silos of scientific research and the growing challenge of information overload limit awareness across the literature and hinder innovation. Algorithmic curation and recommendation, which often prioritize relevance, can further reinforce these informational "filter bubbles." In response, we describe Bridger, a system for facilitating discovery of scholars and their work. We construct a faceted representation of authors with information gleaned from their papers and inferred author personas, and use it to develop an approach that locates commonalities and contrasts between scientists to balance relevance and novelty. In studies with computer science researchers, this approach helps users discover authors considered useful for generating novel research directions. We also demonstrate an approach for displaying information about authors, boosting the ability to understand the work of new, unfamiliar scholars. Our analysis reveals that Bridger connects authors who have different citation profiles and publish in different venues, raising the prospect of bridging diverse scientific communities.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
