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, to explore design tradeoffs between relevant and novel recommendations. 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 ("bridges") and contrasts between scientists -- retrieving partially similar authors rather than aiming for strict similarity. In studies with computer science researchers, this approach helps users discover authors considered useful for generating novel research directions, outperforming a state-of-art neural model. In addition to recommending new content, we also demonstrate an approach for displaying it in a manner that boosts researchers' ability to understand the work of authors with whom they are unfamiliar. Finally, our analysis reveals that Bridger connects authors who have different citation profiles, publish in different venues, and are more distant in social co-authorship networks, raising the prospect of bridging diverse communities and facilitating discovery.
翻译:作为回应,我们描述布里杰(Bridger)这个促进学者发现及其工作的系统,以探索相关和新颖建议之间的设计取舍。我们构建了一个作者的面相代表,其信息从他们的论文中提取出来,并推断出作者人物,并用它来开发一种方法,找出共同点(“桥”)和科学家之间的对比 -- -- 检索部分相似作者,而不是严格相似点。在与计算机科学研究人员的研究中,这一方法帮助用户发现作者认为有助于产生新的研究方向,超越最新神经模型。除了推荐新内容外,我们还展示了一种展示方法,提高研究人员了解他们不熟悉的作者的工作的能力。最后,我们的分析显示布里杰将作者连接在一起,他们拥有不同的引言简介,在不同地点出版,并且更远地便利了社交联系网络和探索前景。