III: Small: Core: Monotonic Retargeting: A Scalable Learning Framework for Determining Order

III：小：核心：单调重定向：用于确定顺序的可扩展学习框架

• 批准号: 1421729
• 负责人: Joydeep Ghosh
• 金额: $ 49.62万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1421729&HistoricalAwards=false
• 关键词: III; Small; Core; Monotonic; Retargeting

Determining preferences, or identifying and ordering items of most interest or relevance based on very limited information, is a fundamental problem in many disciplines. While perhaps most obvious in search, the problem shows up in areas as diverse as economics and health informatics. This project is developing a broad methodology to address challenging learning problems that involve ordering, including determining top choices for recommendation, multi-label classification, and learning to rank-order a set of query results. The key insight is that if the objects to be ordered are given numeric scores, only the relative values of these scores affect their ranking, and not the actual values. This project is using this insight to develop new and better learning algorithms for ranking.Specifically, the project is developing methods that can efficiently optimize over all possible monotonic (that is, order preserving) transformations of scores. Since these scores become the target values for regression, this class of approaches is being called monotonic retargeting. A systematic way of alternating between readjusting scores and updating the regression model is being developed, with nice properties for scalability and distributed implementation, as well as strong convergence guarantees. Themes common to different types of preference learning or ranking studies are being identified to help bring together the diverse communities, including students, that work on this topic. This wide coverage is possible as it easy to relate to the need to determine priorities and make choices in various walks of life. The applications and impacts of the project are expected to be wide and diverse as well.

确定偏好，或基于非常有限的信息识别和排序最感兴趣或相关的项目，是许多学科中的基本问题。 虽然这个问题在搜索领域可能最明显，但它也出现在经济学和健康信息学等不同领域。该项目正在开发一种广泛的方法来解决涉及排序的具有挑战性的学习问题，包括确定推荐的首选项，多标签分类以及学习对一组查询结果进行排序。关键的见解是，如果要排序的对象被赋予数字分数，则只有这些分数的相对值影响它们的排名，而不是实际值。 该项目正在利用这一洞察力开发新的和更好的排名学习算法。具体来说，该项目正在开发可以有效优化所有可能的单调（即保序）转换的方法。由于这些分数成为回归的目标值，这类方法被称为单调重定向。正在开发一种在重新调整分数和更新回归模型之间交替的系统方法，该方法具有良好的可扩展性和分布式实现特性，以及强大的收敛保证。正在确定不同类型的偏好学习或排名研究的共同主题，以帮助汇集从事这一主题工作的不同社区，包括学生。这种广泛的覆盖面是可能的，因为它很容易与确定优先事项和在各行各业作出选择的需要联系起来。预计该项目的应用和影响也将广泛而多样。