SBIR Phase I: Automated Display Optimization Based on Attention Predictions

这个小型企业创新研究(SBIR)第一阶段项目将建立使用人类注意力配置的预测模型来优化商业显示器有效性的可行性。优化显示的传统解决方案需要人工数据。例如，在A/B测试期间，目标受众接触到不同版本的显示器(可比较的)，并使用诸如点击之类的响应来确定有效性。传统优化解决方案的一个关键缺点是，可测试的可比较对象的数量受到可用受众的严重限制。如果能够基于计算机分析而不是人工数据来执行显示优化，这些缺点将被消除。第一阶段的研究将确定计算机生成的注意力分数可以在多大程度上预测通过鼠标点击来衡量的目录选择。预计注意力分数高的目录项将比注意力分数低和中等的目录项被更频繁地选择。每年花费数十亿美元使用需要人工数据的技术来优化商业展示，如A/B和多变量测试、情境和行为定位、消费者研究等。如果成功，这个SBIR项目将产生一个基于注意力预测的软件即服务解决方案，用于优化商业展示。这项拟议的创新将实现基于人类数据几乎不可能执行的大规模显示优化。自动优化的另一个关键优势是，它可以在将观众暴露在无效的展示之前执行。因此，拟议中的创新将增加商业显示器的收入收益。这一创新的解决方案可以应用于各种在线和离线展示，包括目录、货架平面图和图形广告。除了显示优化，注意力模型还可能带来重要的科学技术进步、商业应用和健康益处。

This Small Business Innovation Research (SBIR) Phase I project will establish the feasibility of optimizing the effectiveness of commercial displays using a predictive model of human attention deployment. Traditional solutions for optimizing displays require human data. For example, during A/B testing, the target audience is exposed to alternate versions of a display (comparables) and responses such as clicks are used to determine effectiveness. A key shortcoming of traditional optimization solutions is that the number of comparables that can be tested is severely limited by the available audience. These shortcomings will be eliminated if display optimization could be performed based on computer analyses rather than human data. The Phase I research will establish the extent to which computer-generated attention scores can predict catalog selections as measured by mouse clicks. It is anticipated that catalog items with high attention scores will be selected more frequently than catalog items with low and average attention scores. Billions of dollars are spent annually on optimizing commercial displays using techniques that require human data, such as A/B and multivariate testing, contextual and behavioral targeting, consumer research, etc. If successful, this SBIR project will result in a software-as-a-service solution for optimizing commercial displays based on attention predictions. This proposed innovation will enable large scale display optimizations that are practically impossible to perform based on human data. Another key advantage of automated optimization is that it could be performed before exposing audiences to ineffective displays. As a result, the proposed innovation will increase revenue gains from commercial displays. This innovative solution could be applied to a variety of online and offline displays, including catalogs, shelf plans, and graphic ads. Beyond display optimization, attention models could lead to important scientific and technological advances, commercial applications, and health benefits.