III: Small: Realizing Fairness in Recommender Systems: Intersectionality, Tools, Explanation

III：小：在推荐系统中实现公平性：交叉性、工具、解释

• 批准号: 1911025
• 负责人: Robin Burke
• 金额: $ 49.79万
• 依托单位: University of Colorado at Boulder
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1911025&HistoricalAwards=false
• 关键词: III; Small; Realizing; Fairness; Recommender

Recommender systems provide personalized suggestions to users of e-commerce, social media, and many other types of applications. As they have become more prevalent, recommender systems have moved from areas of consumer taste to areas with greater social impact and sensitivity, such as financial services, employment, and housing. Concern has grown that personalized recommendations may exhibit bias, produce unfair results, and entrench problems of inequity. This limits the potential utility of recommender systems in environments such as employment, where fair treatment of users is legally mandated. Lack of attention to fairness has also meant that recommender systems have tended to reinforce biases and to limit users' exposure to diverse items. In spite of the importance of this issue to the public and the recent work of researchers, there is little progress on key aspects of fairness-aware recommendation. Companies whose sites depend heavily on personalized recommendation therefore have little guidance from the research community about how to apply fairness-aware recommendation and how to evaluate their efforts relative to the state of the art. At the same time, recommender systems researchers have difficulty making progress in the field because of the lack of established datasets and metrics. This project will make advances in fairness-aware recommendation that make it suitable for real-world applications. To meet these needs, the project will develop recommendation models and algorithms that can achieve high accuracy, while preserving fairness in multiple inter-sectional dimensions, and explore their effectiveness in three fairness-critical domains: philanthropy, employment, and news. Existing fairness-aware recommendation algorithms have, with few exceptions, been developed and evaluated in contexts where a single dimension of fairness, defining a single protected group, is considered. The research team will extend these algorithms to be sensitive to multiple protected features, and to incorporate multiple sides of the recommendation transaction. It is well known that explanations support users in their use of recommender systems, engendering greater trust. However, the greater complexity of fairness-aware recommendation makes it difficult to produce explanations, and the introduction of fairness objectives may actually decrease trust in some users who may perceive the system as insufficiently responsive to their interests. The project will therefore develop explanation mechanisms for fairness-aware recommendation that support transparency in the application of fairness criteria. Finally, in order to put fairness-aware recommendation research on a firmer foundation, this project will develop techniques for generating synthetic datasets that can be used in developing and evaluating recommendation algorithms. The project will use latent factor methods to represent patterns of user-item associations, including associations with users of different types, and then apply sampling to these factors to generate synthetic data containing realistic rating patterns. The software developed throughout the project will be incorporated into open-source platforms for the benefit of other researchers.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.

推荐系统向电子商务、社交媒体和许多其他类型的应用的用户提供个性化建议。随着推荐系统变得越来越普遍，推荐系统已经从消费者口味的领域转移到具有更大社会影响和敏感性的领域，例如金融服务，就业和住房。人们越来越担心个性化推荐可能会表现出偏见，产生不公平的结果，并加深不公平的问题。这限制了推荐系统在就业等环境中的潜在效用，在这些环境中，公平对待用户是法律规定的。缺乏对公平性的关注也意味着推荐系统往往会加强偏见，并限制用户接触不同的项目。尽管这一问题对公众的重要性和研究人员最近的工作，有公平意识的建议的关键方面几乎没有进展。因此，网站严重依赖个性化推荐的公司很少有来自研究界的指导，关于如何应用公平意识推荐以及如何评估他们相对于最先进的水平所做的努力。同时，由于缺乏既定的数据集和指标，推荐系统的研究人员很难在该领域取得进展。该项目将在公平性感知推荐方面取得进展，使其适用于现实世界的应用。为了满足这些需求，该项目将开发推荐模型和算法，这些模型和算法可以实现高准确性，同时在多个交叉维度上保持公平性，并探索它们在三个公平关键领域的有效性：慈善事业，就业和新闻。现有的公平意识的推荐算法，除了少数例外，开发和评估的情况下，一个单一的公平性，定义一个单一的保护组，被认为是。研究团队将扩展这些算法，使其对多个受保护的功能敏感，并将推荐交易的多个方面结合起来。众所周知，解释支持用户使用推荐系统，产生更大的信任。然而，更大的复杂性的公平意识的建议，使其难以产生的解释，和公平目标的引入实际上可能会减少信任的一些用户可能会认为系统不足以响应他们的利益。因此，该项目将为公平意识建议制定解释机制，支持公平标准应用的透明度。最后，为了将公平意识推荐研究建立在更坚实的基础上，该项目将开发用于生成合成数据集的技术，这些数据集可用于开发和评估推荐算法。该项目将使用潜在因素方法来表示用户-项目关联的模式，包括与不同类型用户的关联，然后对这些因素进行抽样，以生成包含现实评级模式的合成数据。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。

项目成果

librec-auto: A Tool for Recommender Systems Experimentation

• DOI: 10.1145/3459637.3482006
• 发表时间: 2021-10
• 期刊: Proceedings of the 30th ACM International Conference on Information & Knowledge Management
• 作者: Nasim Sonboli;M. Mansoury;Ziyue Guo;Shreyas Kadekodi;Weiwen Liu;Zijun Liu;Andrew Schwartz;R. Burke

Fairness and Transparency in Recommendation: The Users’ Perspective

• DOI: 10.1145/3450613.3456835
• 发表时间: 2021-03
• 期刊: Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization
• 作者: Nasim Sonboli;Jessie J. Smith;Florencia Cabral Berenfus;R. Burke;Casey Fiesler

Experimentation with fairness-aware recommendation using librec-auto: hands-on tutorial

使用 librec-auto 进行公平感知推荐实验：实践教程

• DOI: 10.1145/3351095.3375670
• 发表时间: 2020
• 期刊: and Transparency
• 作者: Burke, Robin;Mansoury, Masoud;Sonboli, Nasim
• 通讯作者: Sonboli, Nasim

Calibration in Collaborative Filtering Recommender Systems: a User-Centered Analysis

• DOI: 10.1145/3372923.3404793
• 发表时间: 2020-07
• 期刊: Proceedings of the 31st ACM Conference on Hypertext and Social Media
• 作者: Kun-hsien Lin;Nasim Sonboli;B. Mobasher;R. Burke

The Multisided Complexity of Fairness in Recommender Systems

• DOI: 10.1002/aaai.12054
• 发表时间: 2022-06
• 期刊: AI Mag.
• 作者: Nasim Sonboli;R. Burke;Michael D. Ekstrand;Rishabh Mehrotra