EAGER: Fairness-Aware Personalized Recommendations

该项目的目标是创建有效的信息管理员推荐模型，可以为个人用户个性化，同时保持重要的公平性。信息策展人是高质量策展内容的渠道，提供独特的专业知识，决策的可信度和对新内容的访问。这种多样性和由此产生的异质性-在内容类型、社会关系、策展人的动机等方面-对有效的个性化提出了很高的要求。此外，现有的表达偏好，这些策展人的形式喜欢，以下的关系，和其他互动往往是稀疏的。因此，个性化策展人推荐的一个关键挑战是在复杂，嘈杂和异构环境中仔细建模策展人的同时解决稀疏性问题。除了这一挑战之外，目前大多数信息策展人的访问都是通过集中式平台（如搜索引擎、社交网络和传统新闻媒体）进行的，这意味着个人偏好可能与这些平台的目标不一致，导致对策展人的访问可能存在偏见（甚至有限）。馆长推荐中的一个关键问题是如何保持公平性。该项目的预期成果包括可以积极影响现有网络和社交媒体平台的研究进展，以及为信息策展推荐的未来进展提供理论基础。在大规模发现信息策展人，可靠地将用户连接到适当的策展人，并确保这些策展人的公平性方面取得的进展对于支持知情民众的可信信息至关重要。通过将这些研究进展，数据集和工具包带到更广泛的研究社区，该项目可以通过其他研究人员的补充努力来刺激更多的进展。此外，本项目还将开发新的课堂材料，新的推广工作，以及新的扩大参与的讲习班和研讨会。本项目将探索和测试四个具有挑战性的研究问题：（1）在异构环境中学习基于张量的推荐。 由于用户对策展人的偏好可能会受到许多上下文因素的影响，因此第一个任务将直接将用户，策展人，主题和其他因素之间的多种多样的关系直接纳入基于张量的方法中。(2)用于策展人推荐的神经个性化排名。作为这种基于张量的方法的补充，该项目还将探索个性化策展人推荐的新神经模型的功能。神经模型有望在模型设计中提供更大的灵活性，通过激活增加非线性，并相对于基于张量的方法提高性能。 (3)混合神经元+张量模型。第三，该项目将研究新的混合模型，该模型将基于张量的方法（原则性和可解释性）与基于神经的方法（承诺提高性能）的优点相结合。(4)公平意识学习策展。最后，本计画将探讨公平性限制下的个人化推荐。由于用户可能会从用于优化它们的训练数据以及平台目标和个人偏好之间的不一致中继承偏见，因此该项目将构建新的公平意识算法，通过增强主题，策展人，该奖项反映了NSF的法定使命，并被认为是值得通过使用基金会的知识价值和更广泛的影响审查评估的支持的搜索.

The goal of this project is to create effective information curator recommendation models that can be personalized for individual users, while maintaining important fairness properties. Information curators serve as conduits to high-quality curated content, providing unique specialized expertise, trustworthiness in decision-making, and access to novel content. This variety and the resultant heterogeneity -- in terms of content types, social relations, motivations of curators, etc. -- place great demands on effective personalization. Further, existing expressed preferences for these curators in the forms of likes, following relationships, and other interactions are often sparse. Hence, a key challenge for personalized curator recommendation is tackling sparsity while carefully modeling curators in complex, noisy, and heterogeneous environments. Compounding this challenge, most current access to information curators is mediated by centralized platforms (like search engines, social networks, and traditional news media), meaning that personal preferences may not align with the goals of these platforms, leading to potentially biased (or even limited) access to curators. A key question is how to maintain fairness properties in curator recommendation. The expected results of this project include research advances that can positively impact existing web and social media platforms, as well as provide a theoretical foundation for future advances in information curation recommendation. The advances in uncovering information curators at scale, reliably connecting users to appropriate curators, and ensuring fairness-preserving properties of such curators are critical for trustworthy information supporting an informed populace. By bringing these research advances, datasets, and toolkits to the wider research community, this project can spur additional advances from complementary efforts by other researchers. Further, this project will develop new classroom materials, new outreach efforts, and new broadening participation workshops and seminars.This project will explore and test four challenging research problems: (1) Learning Tensor-Based Recommenders in Heterogeneous Environments. Since user preferences for curators may be impacted by many contextual factors, this first task will directly incorporate the multiple and varied relationships among users, curators, topics, and other factors directly into a tensor-based approach. (2) Neural Personalized Ranking for Curator Recommendation. Complementary to such a tensor-based approach, this project will also explore the capabilities of new neural models of personalized curator recommendation. Neural models promise potentially more flexibility in model design, added nonlinearity through activations, and improved performance relative to tensor-based approaches. (3) Hybrid Neural+Tensor Models. Third, this project will investigate new hybrid models that combine the benefits of tensor-based methods (which are principled and interpretable) with neural-based methods (which promise improved performance). (4) Fairness-Aware Learning for Curation. Finally, this project will explore personalized recommendation under fairness-aware constraints. Since recommenders may inherit bias from the training data used to optimize them and from mis-alignment between platform goals and personal preferences, this project will build new fairness-aware algorithms that can empower users by enhancing diversity of topics, curators, and viewpoints.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.