SBIR Phase I: Knowledge Discovery based on Personal Web Content Annotation

这个小企业创新研究第一阶段项目旨在展示基于用户的智能推荐系统的可行性。个人在线研究注释。三个关键特征的结合使这个系统新颖。首先是使用个人网页的注释（高亮、注释中的注释等）来了解用户吗？信息需求。第二种是基于具有共同研究兴趣的相关用户的个人网页注释的社会化推荐。三是将推荐内容直接整合到用户身上？正常的阅读和信息收集行为。总的来说，所提出的系统的这些特性代表了知识发现方面的重大进步。这个项目更广泛的影响包括对研究和教育的贡献，节省了时间、成本和机构和个人在网上寻找任何项目信息的挫败感。在网上搜索时，用户希望做笔记来加强他们对所读内容的理解。在没有足够的在线注释工具的情况下，他们打印重要的Web页面，然后用笔进行注释。从交互式、网络化的Web环境切换到静态、断开的纸张媒介存在两个限制。首先是无法利用这项研究来识别和提取相关内容。其次是研究内容无法被其他有共同研究兴趣的人（如专业同事）获取。这项研究利用了一个正在开发的强大的网络注释系统，该系统允许用户直接对网页进行注释，从而消除了打印的需要。建议的推荐系统将分析用户？注释和建议相关的网页，从而节省了时间、成本和学生、科学家、商业分析师和其他收集在线信息的人所经历的挫折。

This Small Business Innovation Research Phase I project aims to demonstrate the feasibility of an intelligent recommendation system based on users? personal online research annotations. A combination of three key characteristics makes this system novel. First is the use personal Web page annotations (highlights, comments in notes, etc.) to understand users? information needs. Second is social recommendation based on the personal Web page annotations of related users with shared research interests. Third is the integration of recommended content directly into users? normal reading and information gathering behavior. Taken together, these qualities of the proposed system represent a significant advance in knowledge discovery. The broader impact of this project consists of a contribution to research and education by saving time, cost and frustration for institutions and individuals seeking information online for any project. While researching online, users wish to take notes to enforce their understanding of what they read. In the absence of adequate online annotation tools, they print important Web pages to annotate them by pen. Switching from the interactive, networked Web environment to the static, disconnected medium of paper presents two limitations. First is the inability to leverage this research to identify and pull related content. Second is the inaccessibility of the researched content to others with shared research interests such as professional colleagues. The proposed research leverages a powerful web annotation system under development that allows users to directly annotate Web pages and thereby eliminates the need to print. The proposed recommendation system will analyze users? annotations and suggest related Web pages, thereby saving the time, cost and frustration experienced by students, scientists, business analysts and others who aggregate online information.