III: Medium: Collaborative Research: Optimization with Sparse Priors -- Algorithms, Indices, and Economic Incentives

III：媒介：协作研究：稀疏先验优化——算法、指数和经济激励

• 批准号: 0904325
• 负责人: Ashish Goel
• 金额: $ 69.81万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0904325&HistoricalAwards=false
• 关键词: III; Medium; Collaborative; Research; Optimization

This is a collaborative research project combining the expertise of Ashish Goel, Stanford University (IIS-0904325) and Sanjeev Khanna, University of Pennsylvania (IIS-0904314).Traditionally, content has been generated by a limited number of publishers (such as book houses, music companies, and newspapers), and its quality then evaluated by professional editors and reviewers. In recent years, however, individuals have become mass producers of content, generating images, blogs, opinions, and recommendations, in a decentralized manner. This content is then discovered and consumed by other users, and centralized review is rendered infeasible by the sheer magnitude of available content. Consequently, there is a need to utilize user feedback, both explicit and implicit, in order to provide optimum rankings and recommendations to Internet users. The same broad problem occurs in online advertising, automatic moderation of discussion boards, and automated deductions of user preference on social networks. In addition to being very large, user activity data on the Internet is also typically very sparse, since each user only performs a small share of possible actions (e.g., searches for a small fraction of keywords, reviews or purchases a small fraction of products).This project aims to design algorithms and optimization techniques to effectively utilize such data. The sparse data is treated as a "prior belief" on user preferences. The project also aims to design economic incentives to obtain useful and corrective data, robust to manipulation. The two parts of this research interact strongly with each other, since the algorithmic component can identify valuable pieces of additional information to acquire. Together, these two parts can help users derive optimum value from Internet data. Results of this project will improve search engine performance and facilitate web applications that employ user feedback. The project Web site (http://www.stanford.edu/~ashishg/sparse_opt.html) will be used to disseminate results.

这是一个结合了斯坦福大学Ashish Goel（IIS-0904325）和宾夕法尼亚大学Sanjeev卡纳（IIS-0904314）的专业知识的合作研究项目。传统上，内容由有限数量的出版商（如图书公司、音乐公司和报纸）生成，然后由专业编辑和评审员对其质量进行评估。然而，近年来，个人已经成为内容的大规模生产者，以分散的方式生成图像，博客，意见和推荐。 这些内容随后被其他用户发现和消费，并且由于可用内容的绝对数量而使得集中审查变得不可行。因此，有必要利用用户的反馈，无论是明确的和隐含的，以提供最佳的排名和建议，互联网用户。在线广告、讨论板的自动审核以及社交网络上用户偏好的自动扣除也存在同样的广泛问题。除了非常大之外，互联网上的用户活动数据通常也非常稀疏，因为每个用户仅执行可能动作的一小部分（例如，搜索一小部分关键字，评论或购买一小部分产品）。该项目旨在设计算法和优化技术，以有效地利用这些数据。稀疏数据被视为用户偏好的“先验信念”。该项目还旨在设计经济激励措施，以获得有用和正确的数据，防止操纵。这项研究的两个部分相互作用很强，因为算法部分可以识别有价值的额外信息。这两个部分一起可以帮助用户从互联网数据中获得最佳价值。该项目的成果将提高搜索引擎的性能，并促进采用用户反馈的网络应用程序。将利用该项目的网址（http：//www.stanford.edu/tagashishg/sparse_opt.html）来传播成果。