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 …
Day: January 3, 2026
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 …