III: Small: Information Recommendation for Online Scientific Communities

从传统的以个人为基础的科学研究越来越多地转向通过在线科学社区进行更多的合作模式。一个著名的科学在线社区的例子是由HUBzero平台提供支持的nanoHUB.org。nanoHUB通过提供模拟工具、教材和出版物等数千种资源，受到纳米技术社区的广泛欢迎，吸引了9万多名活跃用户。科学在线社区中信息的快速增长要求智能代理能够识别对用户最有价值的信息。现有的信息推荐解决方案不适合在线科学社区。例如，在线科学社区的用户承担不同类型的任务（例如，寻找教材或进行论文实验），需要区分不同任务的推荐，而现有的推荐解决方案没有提供这一功能。此外，来自在线科学社区用户的大量信息是隐性反馈（例如，点击数据）。然而，大多数现有的推荐解决方案都侧重于明确的反馈信息（例如，用户对电影的评分）。提出的研究旨在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。建议的研究重点包括：(1)特定任务建议：估计可能承担的任务，并将估计结果纳入建议过程；(2)智能混合推荐：将协同推荐和基于内容的推荐技术集成在一个模型中，智能地调整基于内容的信息和协同使用信息的权重；(3)隐式反馈的两两比较方法：模型用户？基于两两比较自然假设的概率模型中推荐资源的隐式反馈信息(4)系统开发与评估：将提出的算法集成到HUBzero平台中。研究结果将在精心设计的用户研究以及现实世界的操作环境（即nanoHUB）中进行评估。拟议的研究将在广泛的领域产生实质性的利益。信息推荐工具将被整合到nanoHUB中，以使大量用户受益。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在其他通用社交网络应用程序（如LinkedIn/Facebook）中进行调整和使用。一些研究课题将被整合到pi教授的课程中。pi将鼓励未被充分代表的学生参与研究项目。

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.