III: Small: Collaborative Research: RUI: Learning to Model Sequences

III：小型：协作研究：RUI：学习序列建模

• 批准号: 1217485
• 负责人: Douglas Turnbull
• 金额: $ 18.6万
• 依托单位: Ithaca College
• 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=1217485&HistoricalAwards=false
• 关键词: III; Small; Collaborative; Research; RUI

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) 的教职员工和学生参与的跨学科项目。学习序列预测模型的能力是多个应用问题的组成部分，从机器翻译的语言模型到为掌握某个主题而推荐阅读材料。该项目的目标是开发新的机器学习算法，可以学习难以用属性描述的项目的序列模型。特别是，该项目开发的模型可以根据训练序列自动将项目嵌入到潜在特征空间中，可以集成部分和噪声辅助信息，并且能够对远程依赖性进行建模。由此产生的预测模型有可能在科学和教育领域得到应用，并可以支持经济向在线业务应用的转变。该项目侧重于音乐播放列表的推荐作为主要测试平台。部署的在线音乐推荐系统不仅提供了测试和评估的框架，该应用领域还有助于吸引来自合作机构康奈尔大学和伊萨卡学院（本科院校）的广泛学生，使本科生能够融入研究。项目成果，包括开源软件和带注释的数据集，通过项目网站 (http://www.cs.cornell.edu/People/tj/playlists/) 传播。