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

这个创新的项目以一种新颖的方式探索了一种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。 本计画的目标是发展一个网路学术资讯的品质评估与关联发现架构，并最终建立一个新颖的架构，以支援研究者存取、组织、利用与交换各种类型的学术资讯。拟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。 该框架将利用社会参考网站上公开的学术信息，并检查基于时间段，主题和基于社交网络的措施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此相关的研究的挑战。 然而，如果成功的话，其结果将对许多领域研究人员社区具有相当大的价值。

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.