III: Medium: Towards Inclusive Recommendation Systems with Stakeholder Alignment

III：中：迈向利益相关者联盟的包容性推荐系统

• 批准号: 2312794
• 负责人: Ruoxi Jia
• 金额: $ 115.92万
• 依托单位: Virginia Polytechnic Institute and State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2026-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2312794&HistoricalAwards=false
• 关键词: III; Medium; Towards; Inclusive; Recommendation

As recommender systems continue to impact diverse stakeholders in various aspects of daily life, accommodating their distinct objectives is crucial. Traditional recommendation methodologies have focused solely on optimizing accuracy and related metrics, neglecting other diverse stakeholder-dependent objectives. This project represents a systematic effort to incorporate the objectives of different stakeholders into the design and deployment of a recommender system. This includes characterizing their objectives, designing recommendation approaches that incorporate and optimize different objectives, improving data quality, and understanding the drivers of undesired system behaviors. This comprehensive effort serves the national interest of advancing trustworthy AI and will include outreach initiatives such as competitions, workshops, and interactive demonstrations. This project aims to investigate the fundamental components necessary for designing a recommender system that aligns with the objectives of multiple stakeholders. To achieve this, the research will (i) develop frameworks that use data as a soft metric to capture complex, context-dependent objectives; (ii) design methods to improve data quality and facilitate the alignment with different objectives; (iii) develop game-theoretic recommendation algorithms to achieve a tradeoff in different objectives that is acceptable to all stakeholders; and (iv) develop frameworks that attribute system-wide behaviors to individuals who provide data to a recommender system. Through these efforts, this project significantly expands the foundational knowledge of alignment mechanism design for machine learning systems and broadens our understanding of the impact of data in a multi-stakeholder environment.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

随着推荐系统继续影响日常生活各个方面的不同利益相关者，适应他们不同的目标至关重要。传统的推荐方法只关注于优化准确性和相关指标，忽略了其他不同的依赖于商家的目标。该项目是一个系统的努力，将不同利益相关者的目标纳入推荐系统的设计和部署。这包括描述他们的目标，设计整合和优化不同目标的推荐方法，提高数据质量，以及理解不期望的系统行为的驱动因素。这一全面的努力符合推进值得信赖的人工智能的国家利益，并将包括竞赛、研讨会和互动演示等外联活动。该项目旨在研究设计一个符合多个利益相关者目标的推荐系统所需的基本组成部分。为实现这一目标，研究将（i）开发使用数据作为软指标的框架，以捕获复杂的、依赖于背景的目标;（ii）设计方法，以提高数据质量并促进与不同目标的一致性;（iii）开发博弈论推荐算法，以实现所有利益攸关方都能接受的不同目标的权衡;以及（iv）开发将系统范围的行为归因于向推荐系统提供数据的个体的框架。通过这些努力，该项目大大扩展了机器学习系统对齐机制设计的基础知识，并拓宽了我们对多利益相关者环境中数据影响的理解。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。