CAREER: Learning- and Incentives-Based Techniques for Aggregating Community-Generated Data

职业：基于学习和激励的技术来聚合社区生成的数据

• 批准号: 1054911
• 负责人: Jennifer Vaughan
• 金额: $ 49.67万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-06-01 至 2015-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1054911&HistoricalAwards=false
• 关键词: CAREER; Learning; Incentives; Based; Techniques

The Internet has led to the availability of novel sources of data on the preferences, behaviors, and beliefs of massive communities of users. Both researchers and engineers are eager to aggregate and interpret this data. However, websites sometimes fail to incentivize high-quality contributions, leading to variable quality data. Furthermore, assumptions made by traditional theories of learning break down in these settings.This project seeks to create foundational machine learning models and algorithms to address and explain the issues that arise when aggregating local beliefs across large communities, and to advance the state-of-the-art understanding of how to motivate high quality contributions. The research can be split into three directions:1. Developing mathematical foundations and algorithms for learning from community-labeled data. This direction involves developing learning models for data from disparate (potentially self-interested ormalicious) sources and using insight from these models to design efficient learning algorithms.2. Understanding and designing better incentives for crowdsourcing. This direction involves modeling crowdsourcing contributions to determine which features to include in systems to encourage the highest quality contributions.3. Introducing novel economically-motivated mechanisms for opinion aggregation. This involves formalizing the properties a prediction market should satisfy and making use of ideas from machine learning and optimization to derive tractable market mechanisms satisfying these properties.This research will have clear impact on industry, especially for web-based crowdsourcing. The PI will pursue her long-term goal of attracting and retaining women in computer science via her involvement in workshops and mentoring programs. Results will be disseminated at http://www.cs.ucla.edu/~jenn/projects/CAREER.html.

互联网带来了有关大量用户社区的偏好、行为和信仰的新数据源。研究人员和工程师都渴望汇总和解释这些数据。然而，网站有时无法激励高质量的贡献，导致数据质量参差不齐。此外，传统学习理论所做的假设在这些环境中被打破。该项目旨在创建基础的机器学习模型和算法，以解决和解释在聚合大型社区的当地信仰时出现的问题，并促进对如何激励高质量贡献的最先进的理解。研究方向可分为三个方向： 1．开发从社区标记数据中学习的数学基础和算法。 该方向涉及为来自不同（可能是自利或恶意）来源的数据开发学习模型，并利用这些模型的见解来设计有效的学习算法。2．了解并设计更好的众包激励措施。该方向涉及对众包贡献进行建模，以确定系统中应包含哪些功能，以鼓励最高质量的贡献。3．引入新的经济驱动的意见聚合机制。这涉及到将预测市场应满足的属性形式化，并利用机器学习和优化的思想来导出满足这些属性的易于处理的市场机制。这项研究将对行业产生明显的影响，特别是对于基于网络的众包。 PI 将通过参与研讨会和指导计划来实现吸引和留住计算机科学领域女性的长期目标。 结果将在 http://www.cs.ucla.edu/~jenn/projects/CAREER.html 上发布。