FeedLens:用于个性化知识图谱探索性搜索的多态透镜

Harmanpreet Kaur, Doug Downey, Amanpreet Singh, Evie (Yu-Yen) Cheng, Daniel S. Weld, Jonathan Bragg
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引用次数: 2

摘要

知识图(KGs)的巨大规模和开放性使得对它们进行探索性搜索对用户的认知要求很高。我们引入了一种新技术,多态透镜,通过从基于KG的系统维护的现有偏好模型中获得新的杠杆来改进对KG的探索性搜索,以推荐内容。该方法基于一个简单而有力的观察:在KG中,偏好模型可以重新定位,不仅可以推荐单一基本实体类型的实体(例如,科学文献KG中的论文,电子商务KG中的产品),还可以推荐所有其他类型的实体(例如,作者,会议,机构;卖家,买家)。我们在一个新的系统FeedLens中实现了我们的技术,该系统建立在Semantic Scholar之上,这是一个用于导航科学文献KG的生产系统。FeedLens重用语义学者(Semantic scholar)上现有的偏好模型——人们精心策划的研究提要——作为探索性搜索的透镜。Semantic Scholar用户可以针对不同的兴趣主题策划多个提要/镜头,例如,一个用于以人为中心的人工智能,另一个用于文档嵌入。虽然这些透镜是根据论文定义的,但FeedLens将它们重新用于指导对作者、机构、场所等的搜索。我们的系统设计是基于预期用户的反馈,通过两次试点调查(分别为n = 17和n = 13)。我们通过第三个(主题内)用户研究(n = 15)比较了FeedLens和Semantic Scholar,发现FeedLens增加了用户参与度,同时减少了完成简短文献回顾任务所需的认知努力。我们的定性结果也强调了人们对FeedLens提供的更有效的探索性搜索体验的偏好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
FeedLens: Polymorphic Lenses for Personalizing Exploratory Search over Knowledge Graphs
The vast scale and open-ended nature of knowledge graphs (KGs) make exploratory search over them cognitively demanding for users. We introduce a new technique, polymorphic lenses, that improves exploratory search over a KG by obtaining new leverage from the existing preference models that KG-based systems maintain for recommending content. The approach is based on a simple but powerful observation: in a KG, preference models can be re-targeted to recommend not only entities of a single base entity type (e.g., papers in the scientific literature KG, products in an e-commerce KG), but also all other types (e.g., authors, conferences, institutions; sellers, buyers). We implement our technique in a novel system, FeedLens, which is built over Semantic Scholar, a production system for navigating the scientific literature KG. FeedLens reuses the existing preference models on Semantic Scholar—people’s curated research feeds—as lenses for exploratory search. Semantic Scholar users can curate multiple feeds/lenses for different topics of interest, e.g., one for human-centered AI and another for document embeddings. Although these lenses are defined in terms of papers, FeedLens re-purposes them to also guide search over authors, institutions, venues, etc. Our system design is based on feedback from intended users via two pilot surveys (n = 17 and n = 13, respectively). We compare FeedLens and Semantic Scholar via a third (within-subjects) user study (n = 15) and find that FeedLens increases user engagement while reducing the cognitive effort required to complete a short literature review task. Our qualitative results also highlight people’s preference for this more effective exploratory search experience enabled by FeedLens.
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