III: Small: Information Recommendation for Online Scientific Communities

三：小：在线科学社区的信息推荐

• 批准号: 1017837
• 负责人: Luo Si
• 金额: $ 49.84万
• 依托单位: Purdue University
• 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: 2010-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1017837&HistoricalAwards=false
• 关键词: III; Small; Information; Recommendation; Online

There has been an increasing shift away from traditions of individual based scientific research toward more collaborative models via online scientific communities. One famous example of scientific online communities is nanoHUB.org powered by the HUBzero platform. nanoHUB has been well received by nanotechnology community and has attracted more than 90,000 active users by providing thousands of resources such as simulation tools, teaching materials and publications. The rapid growth of information in scientific online communities demands intelligent agents that can identify the most valuable to the users. Existing solutions of information recommendation are not adequate for online scientific communities. For example, users in online scientific communities undertake different types of tasks (e.g., seeking teaching materials or conducting experiments for dissertation work) and require recommendation that distinguishes different tasks, which is not provided by existing recommendation solutions. Furthermore, a substantial amount of information from users of online scientific communities is implicit feedback (e.g., click through data). However, most existing recommendation solutions focus on explicit feedback information (e.g., user ratings of movies).The proposed research seeks to overcome the limitations of existing recommendation solutions with a new integrated information recommendation framework for online scientific communities. The proposed research thrusts include: (1) Task-Specific Recommendation: estimate possible tasks undertaken and incorporate the estimation results into the process of making recommendation; (2) Intelligent Hybrid Recommendation: integrate collaborative recommendation and content-based recommendation techniques within a single model that intelligently tunes the weights of content based information and collaborative usage information; (3) Pairwise Comparison Approach for Implicit Feedback: model users? implicit feedback information of recommended resources in a probabilistic model with a natural assumption of pairwise comparison; (4) System Development and Evaluation: integrate proposed algorithms into the HUBzero platform. The research results will be evaluated in carefully designed user studies as well as in real world operational environments (i.e., nanoHUB). The proposed research will yield substantial benefits in broad areas. The information recommendation tool will be incorporated into nanoHUB to benefit a large number of users. The source code of proposed algorithms will be released with the HUBzero platform to enable further advance and development in information recommendation. The proposed information recommendation solutions can be adapted and used in other general purpose social network applications like LinkedIn/Facebook. Some research topics will be integrated into the courses that the PIs teach. The PIs will encourage the involvement of underrepresented students in the research project.

有一个越来越多的转变，从传统的个人为基础的科学研究，更多的合作模式，通过在线科学社区。科学在线社区的一个著名例子是由HUBzero平台提供支持的nanoHUB.org。nanoHUB受到纳米技术社区的好评，通过提供数千种资源，如模拟工具，教学材料和出版物，吸引了超过90，000名活跃用户。科学在线社区中信息的快速增长需要能够识别对用户最有价值的智能代理。现有的信息推荐解决方案不适合在线科学社区。例如，在线科学社区中的用户承担不同类型的任务（例如，寻找教学材料或进行论文工作的实验），并且需要区分不同任务的推荐，这是现有推荐解决方案所不能提供的。此外，来自在线科学社区的用户的大量信息是隐式反馈（例如，点击数据）。然而，大多数现有的推荐解决方案集中于显式反馈信息（例如，所提出的研究试图克服现有推荐解决方案的局限性，为在线科学社区提供一个新的集成信息推荐框架。本文提出的研究方向包括：（1）任务特定推荐（Task-Specific Recommendation）：对可能承担的任务进行估计，并将估计结果融入推荐过程中;（2）智能混合推荐（Intelligent Hybrid Recommendation）：将协同推荐和基于内容的推荐技术集成到一个模型中，智能地调整基于内容的信息和协同使用信息的权重;（3）隐性反馈的两两比较法：模型用户？（4）系统开发和评估：将所提出的算法集成到HUBzero平台中。研究结果将在精心设计的用户研究以及真实的世界操作环境中进行评估（即，nanoHUB）。拟议的研究将在广泛的领域产生重大利益。信息推荐工具将被纳入nanoHUB，使大量用户受益。建议算法的源代码将与HUBzero平台一起发布，以进一步推进和发展信息推荐。所提出的信息推荐解决方案可以适应并用于其他通用社交网络应用，如LinkedIn/Facebook。 一些研究课题将被纳入PI教授的课程。PI将鼓励代表性不足的学生参与研究项目。