III: Small: Collaborative Research: Learning to Model Sequences

III：小：协作研究：学习序列建模

• 批准号: 1217686
• 负责人: Thorsten Joachims
• 金额: $ 31.4万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-10-01 至 2016-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1217686&HistoricalAwards=false
• 关键词: III; Small; Collaborative; Research; Learning

This collaborative research project involves faculty and students from Cornell University (IIS-1217686) and Ithaca College (IIS-1217485) in an interdisciplinary project. The ability to learn predictive models of sequences is a component in several application problems, ranging from language models for machine translation to the recommendation of material to read in order to master a subject. The goal of this project is to develop new machine learning algorithms that can learn sequence models for items that are difficult to describe by attributes. In particular, the project develops models that automatically embed items in a latent feature space based on training sequences, that can integrate partial and noisy side information, and that have the ability to model long-range dependencies.The resulting predictive models have a potential to be employed in science and education and can support the economic shift towards online business applications. The project focuses on the recommendation of music playlists as the main testbed. A deployed online music recommendation system not only provides the framework for testing and evaluation, this application domain helps to attract a broad spectrum of students from collaborating institutions, Cornell University and Ithaca College (an undergraduate institution), enabling the integration of undergraduates in the research. Project results, including open source software and annotated data set, are disseminated via the project Web site (http://www.cs.cornell.edu/People/tj/playlists/).

该合作研究项目涉及康奈尔大学（IIS-1217686）和伊萨卡学院（IIS-1217485）的教职员工和学生。学习序列的预测模型的能力是几个应用程序问题的组成部分，从机器翻译的语言模型到为了掌握主题的材料的建议。该项目的目的是开发新的机器学习算法，这些算法可以学习难以通过属性描述的项目的序列模型。特别是，该项目开发了基于培训序列的潜在特征空间中的项目，这些模型可以集成部分和嘈杂的侧面信息，并且具有对长期依赖性建模的能力。所得的预测模型有可能在科学和教育中使用，并可以支持对在线业务应用程序的转变。该项目着重于音乐播放列表作为主要测试台的建议。部署的在线音乐推荐系统不仅为测试和评估提供了框架，该应用领域有助于吸引来自合作机构，康奈尔大学和伊萨卡学院（本科生）的大量学生，从而使本科生在研究中的整合。项目结果（包括开源软件和注释数据集）通过项目网站（http://www.cs.cornell.edu/people/people/tj/playlists/）传播。