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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. The multisided complexity of fairness in recommender systems

   https://doi.org/10.1002/aaai.12054  

   Sonboli, Nasim; Burke, Robin; Ekstrand, Michael; Mehrotra, Rishabh (June 2022, AI Magazine)

   Abstract Recommender systems are poised at the interface between stakeholders: for example, job applicants and employers in the case of recommendations of employment listings, or artists and listeners in the case of music recommendation. In such multisided platforms, recommender systems play a key role in enabling discovery of products and information at large scales. However, as they have become more and more pervasive in society, the equitable distribution of their benefits and harms have been increasingly under scrutiny, as is the case with machine learning generally. While recommender systems can exhibit many of the biases encountered in other machine learning settings, the intersection of personalization and multisidedness makes the question of fairness in recommender systems manifest itself quite differently. In this article, we discuss recent work in the area of multisided fairness in recommendation, starting with a brief introduction to core ideas in algorithmic fairness and multistakeholder recommendation. We describe techniques for measuring fairness and algorithmic approaches for enhancing fairness in recommendation outputs. We also discuss feedback and popularity effects that can lead to unfair recommendation outcomes. Finally, we introduce several promising directions for future research in this area. 

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4. 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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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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