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1. Distributionally-Informed Recommender System Evaluation

   https://doi.org/10.1145/3613455  

   Ekstrand, Michael D.; Carterette, Ben; Diaz, Fernando (August 2023, ACM Transactions on Recommender Systems)

   Current practice for evaluating recommender systems typically focuses on point estimates of user-oriented effectiveness metrics or business metrics, sometimes combined with additional metrics for considerations such as diversity and novelty. In this paper, we argue for the need for researchers and practitioners to attend more closely to various distributions that arise from a recommender system (or other information access system) and the sources of uncertainty that lead to these distributions. One immediate implication of our argument is that both researchers and practitioners must report and examine more thoroughly the distribution of utility between and within different stakeholder groups. However, distributions of various forms arise in many more aspects of the recommender systems experimental process, and distributional thinking has substantial ramifications for how we design, evaluate, and present recommender systems evaluation and research results. Leveraging and emphasizing distributions in the evaluation of recommender systems is a necessary step to ensure that the systems provide appropriate and equitably-distributed benefit to the people they affect. 

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2. A Ranked Bandit Approach for Multi-stakeholder Recommender Systems

   Arabghalizi, Tahereh; Labrinidis, Alexandros (September 2022, Workshop of Multi-Objective Recommender Systems (MORS’22))

   Recommender systems traditionally find the most relevant products or services for users tailored to their needs or interests but they ignore the interests of the other sides of the market (aka stakeholders). In this paper, we propose to use a Ranked Bandit approach for an online multi-stakeholder recommender system that sequentially selects top 𝑘 items according to the relevance and priority of all the involved stakeholders. We presented three different criteria to consider the priority of each stakeholder when evaluating our approach. Our extensive experimental results on a movie dataset showed that the contextual multi-armed bandits with a relevance function make a higher level of satisfaction for all involved stakeholders in the long term. Keywords: Multi-stakeholder Recommender Systems; Multi-armed Bandits; Ranked Bandit; 

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3. XPL-CF: Explainable Embeddings for Feature-based Collaborative Filtering

   https://doi.org/10.1145/3459637.3482221  

   Almutairi, Faisal M.; Sidiropoulos, Nicholas D.; Yang, Bo (October 2021, 30th ACM International Conference on Information and Knowledge Management (CIKM))

   Collaborative filtering (CF) methods are making an impact on our daily lives in a wide range of applications, including recommender systems and personalization. Latent factor methods, e.g., matrix factorization (MF), have been the state-of-the-art in CF, however they lack interpretability and do not provide a straightforward explanation for their predictions. Explainability is gaining momentum in recommender systems for accountability, and because a good explanation can swing an undecided user. Most recent explainable recommendation methods require auxiliary data such as review text or item content on top of item ratings. In this paper, we address the case where no additional data are available and propose augmenting the classical MF framework for CF with a prior that encodes each user's embedding as a sparse linear combination of item embeddings, and vice versa for each item embedding. Our XPL-CF approach automatically reveals these user-item relationships, which underpin the latent factors and explain how the resulting recommendations are formed. We showcase the effectiveness of XPL-CF on real data from various application domains. We also evaluate the explainability of the user-item relationship obtained from XPL-CF through numeric evaluation and case study examples. 

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4. 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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5. The Many Faces of Fairness: Exploring the Institutional Logics of Multistakeholder Microlending Recommendation

   https://doi.org/10.1145/3593013.3594106  

   Smith, Jessie J.; Buhayh, Anas; Kathait, Anushka; Ragothaman, Pradeep; Mattei, Nicholas; Burke, Robin; Voida, Amy (June 2023, FAccT '23: Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency)

   Recommender systems have a variety of stakeholders. Applying concepts of fairness in such systems requires attention to stakeholders’ complex and often-conflicting needs. Since fairness is socially constructed, there are numerous definitions, both in the social science and machine learning literatures. Still, it is rare for machine learning researchers to develop their metrics in close consideration of their social context. More often, standard definitions are adopted and assumed to be applicable across contexts and stakeholders. Our research starts with a recommendation context and then seeks to understand the breadth of the fairness considerations of associated stakeholders. In this paper, we report on the results of a semi-structured interview study with 23 employees who work for the Kiva microlending platform. We characterize the many different ways in which they enact and strive toward fairness for microlending recommendations in their own work, uncover the ways in which these different enactments of fairness are in tension with each other, and identify how stakeholders are differentially prioritized. Finally, we reflect on the implications of this study for future research and for the design of multistakeholder recommender systems. 

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