Modeling User Exploration Saturation: When Recommender Systems Should Stop Pushing Novelty

• URL: http://arxiv.org/abs/2604.16419v1
• Date: Thu, 02 Apr 2026 19:57:27 GMT
• Title: Modeling User Exploration Saturation: When Recommender Systems Should Stop Pushing Novelty
• Abstract summary: We study exploration saturation as a user-dependent phenomenon arising from fairness- and novelty-driven recommendation strategies.<n>Our results indicate that fairness-induced exploration exhibits diminishing or non-monotonic returns across users.<n>These findings suggest that recommendation systems should adapt not only to relevance but also to the amount of fairness-driven exploration applied to individual users.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 which further increases in exploration no longer improve user utility and may instead reduce engagement or perceived relevance. Rather than proposing a new fairness-aware algorithm or optimizing a specific objective, we empirically analyze how increasing exploration affects users across varied recommendation models. Through longitudinal experiments using MovieLens-1M and Last.fm datasets, our results indicate that fairness-induced exploration exhibits diminishing or non-monotonic returns and varies substantially across users. In particular, users with limited interaction histories tend to reach saturation earlier, suggesting that uniform fairness or novelty pressure can disproportionately disadvantage certain users. These findings reveal a trade-off between fairness and user experience, suggesting that recommendation systems should adapt not only to relevance but also to the amount of fairness-driven exploration applied to individual users.

Related papers

• Tree of Preferences for Diversified Recommendation [54.183647833064136]
  We study diversified recommendation from a data-bias perspective.<n>Inspired by the outstanding performance of large language models (LLMs) in zero-shot inference leveraging world knowledge, we propose a novel approach.
  arXiv  Detail & Related papers  (2025-12-24T04:13:17Z)
• Multi-agents based User Values Mining for Recommendation [52.26100802380767]
  We propose a zero-shot multi-LLM collaborative framework for effective and accurate user value extraction.<n>We apply text summarization techniques to condense item content while preserving essential meaning.<n>To mitigate hallucinations, we introduce two specialized agent roles: evaluators and supervisors.
  arXiv  Detail & Related papers  (2025-05-02T04:01:31Z)
• Learning Recommender Systems with Soft Target: A Decoupled Perspective [49.83787742587449]
  We propose a novel decoupled soft label optimization framework to consider the objectives as two aspects by leveraging soft labels. We present a sensible soft-label generation algorithm that models a label propagation algorithm to explore users' latent interests in unobserved feedback via neighbors.
  arXiv  Detail & Related papers  (2024-10-09T04:20:15Z)
• Retrieval Augmentation via User Interest Clustering [57.63883506013693]
  Industrial recommender systems are sensitive to the patterns of user-item engagement. We propose a novel approach that efficiently constructs user interest and facilitates low computational cost inference. Our approach has been deployed in multiple products at Meta, facilitating short-form video related recommendation.
  arXiv  Detail & Related papers  (2024-08-07T16:35:10Z)
• UOEP: User-Oriented Exploration Policy for Enhancing Long-Term User Experiences in Recommender Systems [7.635117537731915]
  Reinforcement learning (RL) has gained traction for enhancing user long-term experiences in recommender systems. Modern recommender systems exhibit distinct user behavioral patterns among tens of millions of items, which increases the difficulty of exploration. We propose User-Oriented Exploration Policy (UOEP), a novel approach facilitating fine-grained exploration among user groups.
  arXiv  Detail & Related papers  (2024-01-17T08:01:18Z)
• Ensuring User-side Fairness in Dynamic Recommender Systems [37.20838165555877]
  This paper presents the first principled study on ensuring user-side fairness in dynamic recommender systems.<n>We propose FAir Dynamic rEcommender (FADE), an end-to-end fine-tuning framework to dynamically ensure user-side fairness over time.<n>We show that FADE effectively and efficiently reduces performance disparities with little sacrifice in the overall recommendation performance.
  arXiv  Detail & Related papers  (2023-08-29T22:03:17Z)
• PARSRec: Explainable Personalized Attention-fused Recurrent Sequential Recommendation Using Session Partial Actions [0.5801044612920815]
  We propose an architecture that relies on common patterns as well as individual behaviors to tailor its recommendations for each person. Our empirical results on Nielsen Consumer Panel dataset indicate that the proposed approach achieves up to 27.9% performance improvement.
  arXiv  Detail & Related papers  (2022-09-16T12:07:43Z)
• Personalizing Intervened Network for Long-tailed Sequential User Behavior Modeling [66.02953670238647]
  Tail users suffer from significantly lower-quality recommendation than the head users after joint training. A model trained on tail users separately still achieve inferior results due to limited data. We propose a novel approach that significantly improves the recommendation performance of the tail users.
  arXiv  Detail & Related papers  (2022-08-19T02:50:19Z)
• Deep Causal Reasoning for Recommendations [47.83224399498504]
  A new trend in recommender system research is to negate the influence of confounders from a causal perspective. We model the recommendation as a multi-cause multi-outcome (MCMO) inference problem. We show that MCMO modeling may lead to high variance due to scarce observations associated with the high-dimensional causal space.
  arXiv  Detail & Related papers  (2022-01-06T15:00:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.