BSF: 2014391: Aggregation Methods for Partial Preferences Overview.

BSF：2014391：部分偏好的聚合方法概述。

• 批准号: 1539856
• 负责人: Julia Stoyanovich
• 金额: $ 5万
• 依托单位: Drexel University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1539856&HistoricalAwards=false
• 关键词: BSF; 2014391; Aggregation; Methods; Partial

Preferences are orders among a collection of items attributed to a population of judges. Preference data comes in a variety of forms,such as ranked lists and pairwise comparisons, and is ubiquitous in a plethora of applications across different domains. Useful and effective analysis of preference data typically involves various forms of aggregation among many judges. This work focuses on the analysis of incomplete preferences, including mining, clustering and aggregation. Models, algorithms, data and software products developed as part of this project will be made publicly available. The work will have an impact on the scientific community, in particular on the analysis of functional genomics data, which is central to many areas of bioinformatics, and on social applications, where it will enable efficient and effective analysis of user preferences. This project develops data analysis methodologies that are geared towards incomplete preferences. This work will establish principles, paradigms, and computing machinery for effective analysis of large datasets of incomplete preferences. To that aim, this project develops (1) novel approaches for mining frequent, or otherwise interesting, patterns in preference data; (2) novel clustering and preference aggregation methods; and (3) an extensive data acquisition and experimental evaluation to enhance the research and development of analysis methodologies. The PI will involve graduate and undergraduate students in her research, and will continue to work with women and under-represented minorities. Both the process and the outcome of this research will be integrated into data management and data science courses taught by the PI.

偏好是归因于法官人口的项目集合之间的命令。 偏好数据有多种形式，例如排名列表和成对比较，并且在跨不同域的大量应用中无处不在。 对偏好数据的有效分析通常涉及许多法官之间的各种形式的聚合。这项工作重点是分析不完整的偏好，包括采矿，聚类和聚合。 将公开可用的模型，算法，数据和软件产品作为该项目的一部分。这项工作将对科学界有影响，特别是对功能基因组数据的分析，这对生物信息学的许多领域以及社交应用是至关重要的，它将能够对用户偏好进行有效的有效分析。 该项目开发了针对不完整偏好的数据分析方法。这项工作将建立原理，范例和计算机械，以有效分析不完整偏好的大型数据集。为了实现这一目标，该项目开发了（1）新颖的方法，以频繁或其他有趣的模式在偏好数据中的模式； （2）新颖的聚类和偏好聚集方法； （3）广泛的数据获取和实验评估，以增强分析方法的研究和开发。 PI将涉及研究生和本科生的研究，并将继续与妇女和代表不足的少数民族合作。 这项研究的过程和结果都将集成到PI教授的数据管理和数据科学课程中。