EAGER: Tapping into Public Academic Information on the Social Web: Towards a Novel Academic Recommendation Framework

EAGER：利用社交网络上的公共学术信息：迈向新颖的学术推荐框架

• 批准号: 1052773
• 负责人: Daqing He
• 金额: $ 5万
• 依托单位: University of Pittsburgh
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-10-01 至 2012-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1052773&HistoricalAwards=false
• 关键词: EAGER; Tapping; into; Public; Academic

AbstractThis innovative project explores in a novel fashion an emerging phenomenon of significant importance to academic research. Public academic information resources on the social web are growing in size and number at a very rapid rate. The goal of this project is to develop a quality assessment and an association discovery framework for online academic information, and ultimately to establish a novel framework for supporting researchers in accessing, organizing, utilizing, and exchanging all types of academic information. The proposed assessment framework will utilize the rich behaviors expressed in social reference data to design quality assessment measures that are tightly connected to the new data. The framework will utilize publicly-available academic information on social reference sites, and examine association rules that connect two articles based on time periods, topics and social-network based measures. The dynamic information environment of the web adds considerably to the challenges associated with this research. However, if successful, the results will be of considerable value to many communities of domain researchers.

摘要这个创新的项目以一种新颖的方式探索了一个对学术研究具有重要意义的新兴现象。社交网络上的公共学术信息资源在规模和数量上都在以非常快的速度增长。该项目的目标是开发一个在线学术信息的质量评估和关联发现框架，并最终建立一个支持研究人员访问、组织、利用和交换各种类型的学术信息的新框架。建议的评估框架将利用社会参考数据中表达的丰富行为来设计与新数据紧密相关的质量评估措施。该框架将利用社交参考网站上公开可用的学术信息，并根据时间段、主题和基于社交网络的衡量标准来审查连接两篇文章的关联规则。网络的动态信息环境大大增加了与这项研究相关的挑战。然而，如果成功，结果将对许多领域研究人员社区具有相当大的价值。