In multi-stakeholder recommender systems, provider-fairness interventions that primarily regulate overall exposure often overlook how different user groups historically prefer different provider groups, which results in recommendation distributions that misalign audience allocation and can introduce new forms of disparity. Preference distribution-aware re-ranking can enable provider-fair visibility while preserving this cross-group preference structure, by aligning recommendation shares …
hopwise: A Python Library for Explainable Recommendation based on Path Reasoning over Knowledge Graphs
Explainable recommendation methods based on path reasoning over knowledge graphs require an end-to-end workflow that connects graph preparation, model training, explanation generation, and explanation-aware evaluation. hopwise is an open-source Python library that extends the RecBole ecosystem with interoperable datasets, path-reasoning models, and explanation-oriented evaluation tools, making systematic benchmarking and reuse practical. Context and motivation Path-based …
Accuracy and beyond-accuracy perspectives of controllable multi-objective recommender systems
In interactive recommendation settings, optimizing primarily for estimated relevance often leads to recommendation lists that over-emphasize familiar and popular items, which can reduce discovery and undermine longer-term value. Individual-level multi-objective control can enable recommendation lists that better reflect heterogeneous user goals, by translating explicit preference signals into objective trade-offs that the recommender is designed to …
Blooming Beats: An Interactive Music Recommender SystemGrounded in TRACE Principles and Data Humanism
In music streaming, personalization is commonly delivered through opaque recommendation pipelines and thin interfaces, which often leads to explanations that are misaligned with listeners’ situated experiences and reduces transparency to a technical afterthought. Interactive narrative visualizations can enable more human-centered transparency and controllability in this setting by linking recommendations to temporally grounded listening patterns and …
PRISM: From Individual Preferences to Group Consensus through Conversational AI-Mediated and Visual Explanations
In group accommodation booking, delegating coordination to messaging apps and informal voting often leads to opaque preference trade-offs and social influence, which results in decisions that reflect dominance or conformity rather than genuine consensus. Conversational elicitation coupled with visual preference alignment can enable groups to surface, compare, and negotiate constraints transparently by separating private preference …
Auditing recommender systems for user empowerment in Very Large Online Platforms under the Digital Services Act
The governance of recommender systems in very large online platforms is expected to change significantly under the Digital Services Act, which introduces new obligations on transparency and user control; however, compliance-oriented implementations can still leave users with limited ability to steer personalization and manage their exposure. In this work, we analyze how three major short-video …
How do users perceive recommender systems’ objectives?
In multi-objective recommender systems, system-side metrics are often used both to optimize and to label user-facing controls, but this practice can misalign with users’ conceptual understanding of objectives, which in turn undermines tuning effectiveness, transparency, and satisfaction. Empirical measurement of perception can enable more interpretable objective controls and more defensible metric choices by explicitly linking …
GreenFoodLens: Sustainability Labels for Food Recommendation
Sustainability-aware food recommender systems require environmental impact signals at the ingredient and recipe level to reason about the consequences of personalized meal suggestions. Yet large-scale food recommendation corpora rarely provide carbon and water footprint estimates that can be robustly aligned with recipe ingredients, limiting comparability and reproducibility. GreenFoodLens is a dataset resource that enriches the …
How Fair is Your Diffusion Recommender Model?
In generative recommender systems, adopting diffusion-based learning primarily for accuracy often reproduces the biased interaction distributions present in historical logs, which results in systematic disparities for both users and items. Fairness-aware auditing can enable responsible diffusion recommendation by revealing when utility gains are obtained through consumer- or provider-side inequities, as instantiated in this study. We …
Small Data, Big Impact: Navigating Resource Limitations in Point-of-Interest Recommendation for Individuals with Autism
In point-of-interest recommendation for people with autism, standard preference-driven recommenders often misalign with sensory sensitivities and severe data scarcity, which can yield suggestions that are hard to trust and potentially harmful for everyday exploration. Knowledge-based reasoning and explanation mechanisms can enable more data-efficient, safety-aware personalization in this setting by explicitly modeling user–place sensory compatibility. Supporting …