EAGER: Fairness-Aware Personalized Recommendations

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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.