Modeling User Exploration Saturation: When Recommender Systems Should Stop Pushing Novelty
Fairness-aware recommender systems often mitigate bias by increasing exposure to under-represented or long-tail content, commonly through mechanisms that promote novelty and diversity. In practice, the strength of such interventions is typically controlled using global hyperparameters, fixed regularization weights, heuristic caps, or offline tuning strategies. These approaches implicitly assume that a single level of exploration is appropriate across users, contexts, and stages of interaction. In this work, we study exploration saturation as a user-dependent phenomenon arising from fairness- and novelty-driven recommendation strategies. We define exploration saturation as the point at whi...
| Authors | Enock O. Ayiku, Evelyn Osei, Emebo Onyeka |
|---|---|
| Primary category | cs.IR |
| Published | 2 April 2026 |
| Last updated at source | 2 April 2026 |
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