Reviews are integral to e-commerce services and products. They contain a wealth of information about the opinions and experiences of users, which can help better understand consumer decisions and improve user experience with products and services. Today, data scientists analyze reviews by developing rules and models to extract, aggregate, and understand information embedded in the review text. However, working with thousands of reviews, which are typically noisy incomplete text, can be daunting without proper tools. Here we first contribute results from an interview study that we conducted with fifteen data scientists who work with review text, providing insights into their practices and challenges. Results suggest data scientists need interactive systems for many review analysis tasks. In response we introduce Teddy, an interactive system that enables data scientists to quickly obtain insights from reviews and improve their extraction and modeling pipelines.
Deep Interactive Segmentation of Medical Images: A Systematic Review and Taxonomy
Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；University Hospital Essen (AöR)（埃森大学医院）；German Cancer Research Center (DKFZ)（德国癌症研究中心）；Helmholtz Imaging（亥姆霍兹成像机构）
Spacewalker: Traversing Representation Spaces for Fast Interactive Exploration and Annotation of Unstructured Data
Institute for AI in Medicine, University Hospital Essen（埃森大学医院AI医学研究所）；Mercedes-Benz Tech Innovation（梅赛德斯-奔驰技术创新公司）；Cancer Research Center Cologne Essen, West German Cancer Center Essen, University Hospital Essen（埃森大学医院西德癌症中心科隆-埃森癌症研究中心）；Institute of Computer Graphics and Vision, Graz University of Technology（格拉茨工业大学计算机图形与视觉研究所）；Department of Physics, TU Dortmund University（多特蒙德工业大学物理系）
Data-Driven Interaction Review of an Ed-Tech Application
Smile and Learn是一家教育科技公司，运营着一个智能图书馆，拥有100多个应用程序、游戏和互动故事，面向2至10岁的儿童及其家人。该平台从与系统的交互中收集了数千个数据点，随后提供报告和建议。考虑到导航所有内容的复杂性，图书馆实现了一个推荐系统。本文的目的是评估两个方面的这种系统集中在儿童：用户探索性行为的建议的顺序的影响，和参与的推荐算法的选择的影响。该评估基于2018年10月15日至2018年12月1日期间收集的数据，需要根据其排序分析对建议进行的点击次数，并进行A/B/C测试，其中将两种标准推荐算法与作为基线的随机推荐进行比较。结果表明，建议的顺序和提出的兴趣之间有直接的联系，以及基于受欢迎程度的建议相对于其他选择的优越性。
