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1. Fairness and Transparency in Recommendation: The Users’ Perspective

   https://doi.org/10.1145/3450613.3456835  

   Sonboli, Nasim; Smith, Jessie J.; Cabral Berenfus, Florencia; Burke, Robin; Fiesler, Casey (June 2021, UMAP '21: Proceedings Of The 29th ACM Conference On User Modeling, Adaptation And Personalization)

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   Though recommender systems are defined by personalization, recent work has shown the importance of additional, beyond-accuracy objectives, such as fairness. Because users often expect their recommendations to be purely personalized, these new algorithmic objectives must be communicated transparently in a fairness-aware recommender system. While explanation has a long history in recommender systems research, there has been little work that attempts to explain systems that use a fairness objective. Even though the previous work in other branches of AI has explored the use of explanations as a tool to increase fairness, this work has not been focused on recommendation. Here, we consider user perspectives of fairness-aware recommender systems and techniques for enhancing their transparency. We describe the results of an exploratory interview study that investigates user perceptions of fairness, recommender systems, and fairness-aware objectives. We propose three features – informed by the needs of our participants – that could improve user understanding of and trust in fairness-aware recommender systems. 

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2. Fairness in Recommendation: Foundations, Methods and Applications

   https://doi.org/10.1145/3610302  

   Li, Yunqi; Chen, Hanxiong; Xu, Shuyuan; Ge, Yingqiang; Tan, Juntao; Liu, Shuchang; Zhang, Yongfeng (July 2023, ACM Transactions on Intelligent Systems and Technology)

   As one of the most pervasive applications of machine learning, recommender systems are playing an important role on assisting human decision making. The satisfaction of users and the interests of platforms are closely related to the quality of the generated recommendation results. However, as a highly data-driven system, recommender system could be affected by data or algorithmic bias and thus generate unfair results, which could weaken the reliance of the systems. As a result, it is crucial to address the potential unfairness problems in recommendation settings. Recently, there has been growing attention on fairness considerations in recommender systems with more and more literature on approaches to promote fairness in recommendation. However, the studies are rather fragmented and lack a systematic organization, thus making it difficult to penetrate for new researchers to the domain. This motivates us to provide a systematic survey of existing works on fairness in recommendation. This survey focuses on the foundations for fairness in recommendation literature. It first presents a brief introduction about fairness in basic machine learning tasks such as classification and ranking in order to provide a general overview of fairness research, as well as introduce the more complex situations and challenges that need to be considered when studying fairness in recommender systems. After that, the survey will introduce fairness in recommendation with a focus on the taxonomies of current fairness definitions, the typical techniques for improving fairness, as well as the datasets for fairness studies in recommendation. The survey also talks about the challenges and opportunities in fairness research with the hope of promoting the fair recommendation research area and beyond. 

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

   https://doi.org/10.1145/3351095.3375670  

   Burke, Robin; Mansoury, Masoud; Sonboli, Nasim (January 2020, Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency)

   The field of machine learning fairness has developed metrics, methodologies, and data sets for experimenting with classification algorithms. However, equivalent research is lacking in the area of personalized recommender systems. This 180-minute hands-on tutorial will introduce participants to concepts in fairness-aware recommendation, and metrics and methodologies in evaluating recommendation fairness. Participants will also gain hands-on experience with conducting fairness-aware recommendation experiments with the LibRec recommendation system using the librec-auto scripting platform, and learn the steps required to configure their own experiments, incorporate their own data sets, and design their own algorithms and metrics. 

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4. Fairness-Aware Tensor-Based Recommendation

   https://doi.org/10.1145/3269206.3271795  

   Zhu, Ziwei; Hu, Xia; Caverlee, James (January 2018, Proceedings of the 27th ACM International Conference on Information and Knowledge Management)

   Tensor-based methods have shown promise in improving upon traditional matrix factorization methods for recommender systems. But tensors may achieve improved recommendation quality while worsening the fairness of the recommendations. Hence, we propose a novel fairness-aware tensor recommendation framework that is designed to maintain quality while dramatically improving fairness. Four key aspects of the proposed framework are: (i) a new sensitive latent factor matrix for isolating sensitive features; (ii) a sensitive information regularizer that extracts sensitive information which can taint other latent factors; (iii) an effective algorithm to solve the proposed optimization model; and (iv) extension to multi-feature and multi-category cases which previous efforts have not addressed. Extensive experiments on real-world and synthetic datasets show that the framework enhances recommendation fairness while preserving recommendation quality in comparison with state-of-the-art alternatives. 

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5. Integrating Individual and Group Fairness for Recommender Systems through Social Choice

   https://doi.org/10.1145/3705328.3748087  

   Aird, Amanda; Štefancová, Elena; Buhayh, Anas; All, Cassidy; Homola, Martin; Mattei, Nicholas; Burke, Robin (September 2025, ACM Conference on Recommender Systems (RecSys 2025))

   Fairness in recommender systems is a complex concept, involving multiple definitions, different parties for whom fairness is sought, and various scopes over which fairness might be measured. Re- searchers seeking fairness-aware systems have derived a variety of solutions, usually highly tailored to specific choices along each of these dimensions, and typically aimed at tackling a single fairness concern, i.e., a single definition for a specific stakeholder group and measurement scope. However, in practical contexts, there are a multiplicity of fairness concerns within a given recommendation application and solutions limited to a single dimension are therefore less useful. We explore a general solution to recommender system fairness using social choice methods to integrate multiple hetero- geneous definitions. In this paper, we extend group-fairness results from prior research to provider-side individual fairness, demon- strating in multiple datasets that both individual and group fairness objectives can be integrated and optimized jointly. We identify both synergies and tensions among different objectives with individ- ual fairness correlated with group fairness for some groups and anti-correlated with others. 

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