Unpacking Subjective Creativity Ratings: Using Embeddings to Explain and Measure Idea Novelty-猫眼课题宝

DOI：

10.1115/detc2018-85470

发表时间：

2018-08

期刊：

影响因子：

通讯作者：

中科院分区：

其他

文献类型：

作者：

Faez Ahmed;M. Fuge;Samuel T. Hunter;Scarlett R. Miller

评估设计理念之间的相似性是许多衡量新颖性的设计评估的固有部分。在此类评估任务中，人类擅长在不同的知识集之间建立心理联系，并根据其独特性对想法进行评分。然而，他们对新颖性的决定往往是主观的并且难以解释。在本文中，我们演示了一种使用二维创意图来揭示人类对设计创意相似性的判断的方法。我们通过要求人类进行简单的相似性比较来得出这些地图，其形式为“想法 A 与想法 B 更相似还是与想法 C 更相似？”我们表明，这些地图可以洞察想法之间的关系，并有助于理解该领域。我们还建议，可以通过测量这些地图上的物品的距离来估计想法的新颖性。我们通过对彩色多边形（已知答案）和牛奶起泡器（未知答案）草图的两个数据集进行实验评估来展示我们的方法。我们表明，这些地图揭示了评估者在判断想法相似性时考虑的因素。我们还展示了当数据较少时地图如何变化*解决与该作者的所有通信。提供可用或错误/噪音评级。该方法提供了一个新的研究方向，即通过结合人类判断和计算来导出真实新颖性指标

Assessing similarity between design ideas is an inherent part of many design evaluations to measure novelty. In such evaluation tasks, humans excel at making mental connections among diverse knowledge sets and scoring ideas on their uniqueness. However, their decisions on novelty are often subjective and difficult to explain. In this paper, we demonstrate a way to uncover human judgment of design idea similarity using two dimensional idea maps. We derive these maps by asking humans for simple similarity comparisons of the form “Is idea A more similar to idea B or to idea C?” We show that these maps give insight into the relationships between ideas and help understand the domain. We also propose that the novelty of ideas can be estimated by measuring how far items are on these maps. We demonstrate our methodology through the experimental evaluations on two datasets of colored polygons (known answer) and milk frothers (unknown answer) sketches. We show that these maps shed light on factors considered by raters in judging idea similarity. We also show how maps change when less data is ∗Address all correspondence to this author. available or false/noisy ratings are provided. This method provides a new direction of research into deriving ground truth novelty metrics by combining human judgments and computational

Unpacking Subjective Creativity Ratings: Using Embeddings to Explain and Measure Idea Novelty

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