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Found 22 papers, 9 papers with code
How Fair is Your Diffusion Recommender Model?
Diffusion-based recommender systems have recently proven to outperform traditional generative recommendation approaches, such as variational autoencoders and generative adversarial networks.
Fair Augmentation for Graph Collaborative Filtering
Despite emerging regulations addressing fairness of automated systems, unfairness issues in graph collaborative filtering remain underexplored, especially from the consumer's perspective.
User Modeling and User Profiling: A Comprehensive Survey
This survey serves as a comprehensive resource for researchers and practitioners, offering insights into the evolution of user modeling and profiling and guiding the development of more personalized, ethical, and effective AI systems.
A Cost-Sensitive Meta-Learning Strategy for Fair Provider Exposure in Recommendation
When devising recommendation services, it is important to account for the interests of all content providers, encompassing not only newcomers but also minority demographic groups.
Robustness in Fairness against Edge-level Perturbations in GNN-based Recommendation
Efforts in the recommendation community are shifting from the sole emphasis on utility to considering beyond-utility factors, such as fairness and robustness.
MOReGIn: Multi-Objective Recommendation at the Global and Individual Levels
The state-of-the-art MORSs either operate at the global or individual level, without assuming the co-existence of the two perspectives.
Faithful Path Language Modeling for Explainable Recommendation over Knowledge Graph
This mechanism ensures zero incidence of corrupted paths by enforcing adherence to valid KG connections at the decoding level, agnostic of the underlying model architecture.
Counterfactual Graph Augmentation for Consumer Unfairness Mitigation in Recommender Systems
In recommendation literature, explainability and fairness are becoming two prominent perspectives to consider.
Looks Can Be Deceiving: Linking User-Item Interactions and User's Propensity Towards Multi-Objective Recommendations
In this paper, we present the results of a user study in which we monitored the way users interacted with recommended items, as well as their self-proclaimed propensities towards relevance, novelty and diversity objectives.
GNNUERS: Fairness Explanation in GNNs for Recommendation via Counterfactual Reasoning
Moreover, an empirical evaluation of the perturbed network uncovered relevant patterns that justify the nature of the unfairness discovered by the generated explanations.
Knowledge is Power, Understanding is Impact: Utility and Beyond Goals, Explanation Quality, and Fairness in Path Reasoning Recommendation
Path reasoning is a notable recommendation approach that models high-order user-product relations, based on a Knowledge Graph (KG).
Reinforcement Recommendation Reasoning through Knowledge Graphs for Explanation Path Quality
However, the existing explainable recommendation approaches based on KG merely optimize the selected reasoning paths for product relevance, without considering any user-level property of the paths for explanation.
Post Processing Recommender Systems with Knowledge Graphs for Recency, Popularity, and Diversity of Explanations
Existing explainable recommender systems have mainly modeled relationships between recommended and already experienced products, and shaped explanation types accordingly (e. g., movie "x" starred by actress "y" recommended to a user because that user watched other movies with "y" as an actress).
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Regulating Group Exposure for Item Providers in Recommendation
Engaging all content providers, including newcomers or minority demographic groups, is crucial for online platforms to keep growing and working.
Robust Reputation Independence in Ranking Systems for Multiple Sensitive Attributes
A notable example is represented by reputation-based ranking systems, a class of systems that rely on users' reputation to generate a non-personalized item-ranking, proved to be biased against certain demographic classes.
Consumer Fairness in Recommender Systems: Contextualizing Definitions and Mitigations
In this paper, we conduct a systematic analysis of mitigation procedures against consumer unfairness in rating prediction and top-n recommendation tasks.
Connecting User and Item Perspectives in Popularity Debiasing for Collaborative Recommendation
We characterize the recommendations of representative algorithms by means of the proposed metrics, and we show that the item probability of being recommended and the item true positive rate are biased against the item popularity.
Reputation (In)dependence in Ranking Systems: Demographics Influence Over Output Disparities
In this paper, we formulate the concept of disparate reputation (DR) and study if users characterized by sensitive attributes systematically get a lower reputation, leading to a final ranking that reflects less their preferences.
ECIR 2020 Workshops: Assessing the Impact of Going Online
In this report, we describe the experience of organizing the ECIR 2020 Workshops in this scenario from two perspectives: the workshop organizers and the workshop participants.
A Robust Reputation-based Group Ranking System and its Resistance to Bribery
Also, by clustering users, the effect of bribery in the proposed multipartite ranking system is dimmed, comparing to the bipartite case.
