III: Small: Core: Monotonic Retargeting: A Scalable Learning Framework for Determining Order
III: Small: Core: Monotonic Retargeting: A Scalable Learning Framework for Determining Order
批准号:
1421729
负责人:
Joydeep Ghosh
金额:
$49.62万
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
项目状态:
已结题
起止时间:
2014-09-01 至 2018-08-31
中文摘要
确定偏好,或根据非常有限的信息确定和排序最感兴趣或最相关的项目,在许多学科中是一个基本问题。虽然这个问题可能在搜索领域最为明显,但在经济和卫生信息学等不同领域都出现了这个问题。该项目正在开发一种广泛的方法,以解决涉及排序的挑战性学习问题,包括确定推荐的最佳选择、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。这个项目正在利用这个洞察力来开发新的和更好的排名学习算法。具体地说,该项目正在开发能够有效地优化所有可能的单调(即,保序)分数变换的方法。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目标。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,具有良好的可扩展性和分布式实施特性,以及强大的收敛保证。正在确定不同类型偏好学习或排名研究的共同主题,以帮助将从事这一主题工作的不同社区,包括学生,聚集在一起。这种广泛的报道是可能的,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是广泛和多样的。
英文摘要
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.
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