EAGER: Adaptive Methods for Scalable Dissemination and Retrieval of Scientific Information

EAGER：科学信息可扩展传播和检索的自适应方法

• 批准号: 1142251
• 负责人: Paul Kantor
• 金额: $ 29.95万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-08-15 至 2013-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1142251&HistoricalAwards=false
• 关键词: EAGER; Adaptive; Methods; Scalable; Dissemination

This project seeks dramatically improved access to, and dissemination of, scientific information. Working with cooperating scientific users, it exploits synergies among three important innovations. These are: (1) adaptive and domain specific automatic derivation of topical representations. These topics describe both the documents in the collection, and the interests of the users, during particular searches. The topics support mechanisms for collaborative recommendation, and for exploring the precise contours of each user,s need. (2) Recognition that a combination or set of several items, together, is worth much more (or perhaps much less) than the sum of the values of the items individually. The arXiv experimental system (arXiv_XS) uses topics, and user feedback, to model the complexity of the user's need and interests. (3) Based on these innovations, the system can probe user's interest, selecting items where the user's feedback greatly improves the system's model of that user and his or her search. This "exploration" is designed to improve the systems performance, with minimal degradation of the current search. All these innovations are studied together with complex experimental design and statistical analysis; users may also volunteer to be interviewed, by the researchers, to provide richer information about their experiences with the system. Researchers from Rutgers, Cornell and Princeton lead the project.This exploratory project focuses on the following tasks: (1) develop a richly instrumented voluntary alternative interface to the arXiv, with suitable IRB consent materials supporting active user feedback in the research process, as users search; (2) implement three specific innovative technologies (topics, sets, probes); (3) study their impact on system effectiveness, using experimental design and well-defined performance measures; (4) collect rich user assessments, by telephone and online interviews; (5) assess scalability with respect to the size of the collection, and the size of the "communities of interest" that define the topical user models; (6) seek relations at other domain-specific archives, for potential future studies. If successful, this research will refute a perception that improvement in access and dissemination of scientific literature requires massive techniques adapted from the commercial models for recommender systems and crowd-sourcing. This research will also add to on experimental design, user modeling, and the study of active learning and exploratory system designs.This research will accelerate the production and sharing of scientific information, initially at the arXiv, and subsequently, wherever these innovations are implemented. The research aims to enable researchers who never meet each other to form an "invisible college" by enriching the arXiv systems understanding of all of its users. The project entails some risks, as users may be unwilling to share information about their research interests. While malevolent persons might seek to spam the system, falsely marking information as useful, it is anticipated that scientific communities will generate far less spam than does the world at large. Results of the research will be made available to other researchers, and incorporated in courses at all three universities. The Web site (http://arxiv_xs.rutgers.edu) is used to disseminate information and results from this project.

该项目力求大大改善科学信息的获取和传播。它与合作的科学用户合作，利用三个重要创新之间的协同作用。这些是：（1）自适应和领域特定的主题表示的自动推导。这些主题描述集合中的文档以及用户在特定搜索期间的兴趣。这些主题支持协作推荐的机制，以及探索每个用户需求的精确轮廓的机制。(2)认识到一个组合或一组几个项目，在一起，是值得更多（或可能少得多）的价值比总和的项目单独。arXiv实验系统（arXiv_XS）使用主题和用户反馈来模拟用户需求和兴趣的复杂性。(3)基于这些创新，系统可以探测用户的兴趣，选择用户反馈的项目，极大地改善了该用户及其搜索的系统模型。这种“探索”的目的是提高系统的性能，与当前搜索的最小退化。所有这些创新都是通过复杂的实验设计和统计分析来研究的;用户也可以自愿接受研究人员的采访，以提供有关他们使用该系统的经验的更丰富的信息。来自罗格斯大学、康奈尔大学和普林斯顿大学的研究人员领导了该项目。这个探索性项目的重点是以下任务：（1）开发arXiv的丰富工具的自愿替代接口，并提供合适的IRB同意材料，支持研究过程中用户搜索时的主动反馈;（2）实施三项具体的创新技术（主题，集合，探测器）;（3）研究它们对系统有效性的影响，使用实验设计和定义良好的性能测量;（4）通过电话和在线访谈收集丰富的用户评估;（5）评估集合规模的可扩展性，以及定义主题用户模型的“兴趣社区”的规模;（6）在其他特定领域的档案中寻找关系，以供未来研究。如果成功的话，这项研究将驳斥一种看法，即改善科学文献的获取和传播需要大量的技术，这些技术是从推荐系统和众包的商业模式中改编而来的。这项研究还将增加对实验设计，用户建模，主动学习和探索性系统设计的研究。这项研究将加速科学信息的生产和共享，首先是在arXiv，随后，无论这些创新在哪里实施。这项研究旨在通过丰富arXiv系统对所有用户的理解，使从不见面的研究人员能够形成一个“无形的大学”。该项目带来了一些风险，因为用户可能不愿意分享有关其研究兴趣的信息。虽然恶意的人可能会试图向系统发送垃圾邮件，错误地将信息标记为有用，但预计科学界产生的垃圾邮件将远远少于整个世界。研究结果将提供给其他研究人员，并纳入所有三所大学的课程。网站（http：//arxiv_xs.rutgers.edu）用于传播该项目的信息和成果。