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arxiv: 2604.16419 · v1 · submitted 2026-04-02 · 💻 cs.IR · cs.AI· cs.LG

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

Enock O. Ayiku , Evelyn Osei , Emebo Onyeka This is my paper

Pith reviewed 2026-05-13 20:38 UTC · model grok-4.3

classification 💻 cs.IR cs.AIcs.LG
keywords recommender systemsfairnessexplorationsaturationnoveltyuser modelingdiversity
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The pith

Fairness-driven novelty in recommenders shows diminishing returns that vary by user history length.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper investigates exploration saturation in fairness-aware recommender systems, where adding more novel content eventually stops helping users and can even reduce their engagement. The authors analyze this effect across different users using experiments on MovieLens and Last.fm data. They find that the point of saturation differs markedly between users, arriving earlier for those with fewer past interactions. The work argues that fixed levels of exploration, common in current fairness approaches, overlook these differences and may harm some users' experience. The implication is that systems should adjust exploration dynamically based on individual user responses rather than applying uniform pressure.

Core claim

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.

What carries the argument

Exploration saturation, the point at which further increases in exploration no longer improve user utility and may instead reduce engagement or perceived relevance.

If this is right

  • Uniform application of exploration can disadvantage users with limited histories.
  • Recommendation systems need to adapt the amount of fairness-driven exploration to individual users.
  • Trade-offs between fairness goals and maintaining user engagement must be managed dynamically.
  • Results from longitudinal experiments indicate non-monotonic effects on utility.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Real-time user feedback could be used to detect saturation thresholds during interaction sessions.
  • This user-dependent saturation might extend to other bias mitigation techniques beyond novelty promotion.
  • Models could be developed to predict a user's saturation point from early interaction patterns.

Load-bearing premise

The chosen datasets and models accurately reflect real-world user responses to varying exploration levels without major confounding factors from the experimental setup.

What would settle it

A live user study applying increasing levels of novelty to recommendations and measuring when individual users' engagement metrics stop improving or begin to decline.

Figures

Figures reproduced from arXiv: 2604.16419 by Emebo Onyeka, Enock O. Ayiku, Evelyn Osei.

Figure 1
Figure 1. Figure 1: Utility vs. exploration on Last.fm across recommen [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Utility vs. exploration on MovieLens-1M across [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Utility vs. semantic exploration for NCF. [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 5
Figure 5. Figure 5: Utility vs. exploration for LightGCN [PITH_FULL_IMAGE:figures/full_fig_p005_5.png] view at source ↗
Figure 8
Figure 8. Figure 8: Marginal effect of exploration on utility across rec [PITH_FULL_IMAGE:figures/full_fig_p006_8.png] view at source ↗
Figure 7
Figure 7. Figure 7: Marginal effect of exploration on utility across rec [PITH_FULL_IMAGE:figures/full_fig_p006_7.png] view at source ↗
Figure 9
Figure 9. Figure 9: Distribution of user-level exploration saturation [PITH_FULL_IMAGE:figures/full_fig_p007_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: User-level exploration saturation for NCF on [PITH_FULL_IMAGE:figures/full_fig_p007_10.png] view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The manuscript empirically studies exploration saturation as a user-dependent phenomenon in fairness-aware recommender systems. It claims that increasing novelty and diversity interventions produce diminishing or non-monotonic returns on user utility, with users having limited interaction histories reaching saturation earlier than others. The analysis is conducted via longitudinal experiments on the MovieLens-1M and Last.fm datasets across multiple recommendation models, without proposing new algorithms.

Significance. If the central empirical patterns hold after addressing metric validity concerns, the work would be significant for highlighting trade-offs between global fairness interventions and individualized user experience. It provides evidence against one-size-fits-all exploration hyperparameters and motivates adaptive per-user strategies, which could improve both fairness and long-term engagement in production systems.

major comments (3)
  1. [Abstract and Experimental Methodology] The definition and operationalization of 'exploration saturation' and 'user utility' are not specified with sufficient precision. The abstract refers to utility improvements or declines but does not state the exact offline metric (recall@K, NDCG@K, etc.), the threshold or detection method for saturation (e.g., first point of no further gain or statistically significant drop), or how these are computed across increasing exploration levels.
  2. [Results on User History Lengths] The central claim that short-history users reach saturation earlier may be confounded by properties of offline evaluation on static datasets. As exploration increases, recommendations become more long-tailed; the resulting mismatch with held-out test interactions will produce larger metric variance for users with small test sets, which can artifactually appear as earlier saturation without reflecting behavioral disengagement.
  3. [Longitudinal Experiments] No statistical tests, confidence intervals, or controls for model-specific effects are reported to support the claims of 'consistent patterns across datasets' and 'substantial variation across users.' This weakens the ability to distinguish genuine non-monotonic returns from noise or dataset artifacts.
minor comments (2)
  1. [Introduction] Notation for exploration strength (e.g., regularization weights or novelty caps) should be introduced earlier and used consistently when describing how levels are varied.
  2. [Figures] Figure captions and axis labels for utility-vs-exploration curves would benefit from explicit mention of the metric and the number of users or runs averaged.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript studying exploration saturation in fairness-aware recommender systems. We provide detailed responses to each major comment and indicate the revisions we plan to make.

read point-by-point responses

  1. Referee: [Abstract and Experimental Methodology] The definition and operationalization of 'exploration saturation' and 'user utility' are not specified with sufficient precision. The abstract refers to utility improvements or declines but does not state the exact offline metric (recall@K, NDCG@K, etc.), the threshold or detection method for saturation (e.g., first point of no further gain or statistically significant drop), or how these are computed across increasing exploration levels.

    Authors: We acknowledge that the definitions require more precision for clarity. In the revised manuscript, we will update the abstract and add a dedicated subsection in the methodology to define exploration saturation as the exploration level beyond which user utility no longer improves. User utility will be explicitly tied to the offline evaluation metric employed in our experiments. Saturation will be detected as the point where the metric plateaus or declines, computed by incrementally varying the exploration parameter and observing the utility curve for each user. This will allow readers to replicate the analysis precisely. revision: yes

  2. Referee: [Results on User History Lengths] The central claim that short-history users reach saturation earlier may be confounded by properties of offline evaluation on static datasets. As exploration increases, recommendations become more long-tailed; the resulting mismatch with held-out test interactions will produce larger metric variance for users with small test sets, which can artifactually appear as earlier saturation without reflecting behavioral disengagement.

    Authors: We appreciate this point on potential confounding factors in offline evaluations. The use of static datasets can indeed introduce variance, particularly for users with limited test interactions, as recommendations become more diverse and less aligned with the test set. To address this, we will include a discussion of this limitation in the revised paper and conduct additional experiments stratifying results by test set size to verify if the earlier saturation for short-history users persists. We maintain that the observed patterns are meaningful as they hold across datasets, but we will qualify our claims accordingly. revision: partial

  3. Referee: [Longitudinal Experiments] No statistical tests, confidence intervals, or controls for model-specific effects are reported to support the claims of 'consistent patterns across datasets' and 'substantial variation across users.' This weakens the ability to distinguish genuine non-monotonic returns from noise or dataset artifacts.

    Authors: We agree that the absence of statistical tests limits the robustness of our conclusions. The revised version will incorporate confidence intervals around the utility metrics for different exploration levels and user groups. We will also apply appropriate statistical tests, such as repeated measures ANOVA, to assess the significance of non-monotonic trends and differences between user history length groups. Results will be broken down by individual models to control for model-specific variations, and we will report these in the results section. revision: yes

Circularity Check

0 steps flagged

No circularity: purely empirical analysis of exploration effects on static datasets

full rationale

The paper conducts longitudinal experiments on MovieLens-1M and Last.fm to measure how increasing exploration levels affect offline utility metrics (recall, NDCG) across users with varying history lengths. No equations, derivations, fitted parameters renamed as predictions, or self-citation chains appear in the provided text or abstract. The central claim—that saturation occurs earlier for short-history users—is presented as an observed pattern from the data rather than a mathematical reduction to inputs. This is a standard empirical setup whose validity rests on external dataset properties and metric definitions, not on any self-referential construction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on standard domain assumptions in recommender systems research and public datasets without introducing free parameters or new entities.

axioms (1)
  • domain assumption Engagement metrics reliably proxy user utility under varying exploration levels
    Saturation is defined in terms of utility and engagement changes.

pith-pipeline@v0.9.0 · 5536 in / 1046 out tokens · 56119 ms · 2026-05-13T20:38:13.501976+00:00 · methodology

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Reference graph

Works this paper leans on

44 extracted references · 44 canonical work pages

  1. [1]

    Himan Abdollahpouri, Robin Burke, and Bamshad Mobasher. 2017. Controlling popularity bias in learning-to-rank recommendation. InProceedings of the eleventh ACM conference on recommender systems. 42–46

    work page 2017

  2. [2]

    Gediminas Adomavicius and Alexander Tuzhilin. 2011. Context-Aware Recom- mender Systems.ACM Transactions on Information Systems23, 1 (2011), 103–145

    work page 2011

  3. [3]

    Areeb, Mohammad Nadeem, S

    Q. Areeb, Mohammad Nadeem, S. Sohail, Raza Imam, F. Doctor, Yassine Himeur, Amir Hussain, and A. Amira. 2023. Filter bubbles in recommender systems: Fact or fallacy—A systematic review.Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery13 (2023). doi:10.1002/widm.1512

    work page doi:10.1002/widm.1512 2023

  4. [4]

    Tesfaye Fenta Boka, Zhendong Niu, and Rama Bastola Neupane. 2024. A sur- vey of sequential recommendation systems: Techniques, evaluation, and future directions.Information Systems125 (2024), 102427

    work page 2024

  5. [5]

    Óscar Celma. 2008. Music Recommendation and Discovery in the Long Tail. International Journal of Multimedia Information Retrieval(2008)

    work page 2008

  6. [6]

    Òscar Celma. 2010. lastfm Music Recommendation Dataset (Last.fm-360K and Last.fm-1K). Zenodo. doi:10.5281/zenodo.6090214 Contains user–artist listening count data collected via the Last.fm API

    work page doi:10.5281/zenodo.6090214 2010

  7. [7]

    Zhihong Chen, Rong Xiao, Chenliang Li, Gangfeng Ye, Haochuan Sun, and Hongbo Deng. 2020. Esam: Discriminative domain adaptation with non-displayed items to improve long-tail performance. InProceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval. 579– 588

    work page 2020

  8. [8]

    Yingqiang Ge, Shuxin Zhao, Yixiang Jiang, and Philip S. Yu. 2021. Towards Long- Term Fairness in Recommendation. InProceedings of the 14th ACM International Conference on Web Search and Data Mining (WSDM). 445–453

    work page 2021

  9. [9]

    F Maxwell Harper and Joseph A Konstan. 2015. The movielens datasets: History and context.Acm transactions on interactive intelligent systems (tiis)5, 4 (2015), 1–19

    work page 2015

  10. [10]

    Naieme Hazrati and Francesco Ricci. 2024. Choice models and recommender systems effects on users’ choices.User Modeling and User-Adapted Interaction34, 1 (2024), 109–145

    work page 2024

  11. [11]

    Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yongdong Zhang, and Meng Wang

    work page

  12. [12]

    InProceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval

    LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation. InProceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 639–648

    work page

  13. [13]

    Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017. Neural Collaborative Filtering. InProceedings of the 26th International World Wide Web Conference (WWW). 173–182

    work page 2017

  14. [14]

    Szymczak

    Jianchang Hu and S. Szymczak. 2022. A review on longitudinal data analysis with random forest.Briefings in Bioinformatics24 (2022). doi:10.1093/bib/bbad002

    work page doi:10.1093/bib/bbad002 2022

  15. [15]

    Marius Kaminskas and Derek Bridge. 2016. Diversity, serendipity, novelty, and coverage: a survey and empirical analysis of beyond-accuracy objectives in recommender systems.ACM Transactions on Interactive Intelligent Systems (TiiS) 7, 1 (2016), 1–42

    work page 2016

  16. [16]

    Saeedeh Karimi, Hossein A Rahmani, Mohammadmehdi Naghiaei, and Leila Safari. 2023. Provider fairness and beyond-accuracy trade-offs in recommender systems.arXiv preprint arXiv:2309.04250(2023)

    work page arXiv 2023

  17. [17]

    Dede Kiswanto, Dade Nurjanah, and Rita Rismala. 2018. Fairness aware regular- ization on a learning-to-rank recommender system for controlling popularity bias in e-commerce domain. In2018 International Conference on Information Technology Systems and Innovation (ICITSI). IEEE, 16–21

    work page 2018

  18. [18]

    Bart P Knijnenburg, Martijn C Willemsen, Zeno Gantner, Hakan Soncu, and Chris Newell. 2012. Explaining the user experience of recommender systems. User modeling and user-adapted interaction22, 4 (2012), 441–504

    work page 2012

  19. [19]

    Lihong Li, Wei Chu, John Langford, and Robert Schapire. 2010. A contextual- bandit approach to personalized news article recommendation. InWWW

    work page 2010

  20. [20]

    Zhongzhou Liu, Yuan Fang, and Min Wu. 2023. Mitigating popularity bias for users and items with fairness-centric adaptive recommendation.ACM Transac- tions on Information Systems41, 3 (2023), 1–27

    work page 2023

  21. [21]

    Changhua Pei, Yi Zhang, Yongfeng Zhang, Fei Sun, Xiao Lin, Hanxiao Sun, Jian Wu, Peng Jiang, Junfeng Ge, Wenwu Ou, and Dan Pei. 2019. Personalized re-ranking for recommendation. InProceedings of the 13th ACM Conference on Recommender Systems(Copenhagen, Denmark)(RecSys ’19). Association for Computing Machinery, New York, NY, USA, 3–11. doi:10.1145/3298689.3347000

    work page doi:10.1145/3298689.3347000 2019

  22. [22]

    Minjing Peng, Zhicheng Xu, and Haiyang Huang. 2021. How does information overload affect consumers’ online decision process? An event-related potentials study.Frontiers in Neuroscience15 (2021), 695852

    work page 2021

  23. [23]

    Filip Radlinski, Robert Kleinberg, and Thorsten Joachims. 2008. Too much diver- sity is as bad as too little: The importance of relevance in diversified search. In SIGIR

    work page 2008

  24. [24]

    Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme

    work page

  25. [25]

    InProceedings of the 25th Conference on Uncertainty in Artificial Intelligence (UAI)

    BPR: Bayesian Personalized Ranking from Implicit Feedback. InProceedings of the 25th Conference on Uncertainty in Artificial Intelligence (UAI). 452–461

    work page

  26. [26]

    2015.Recommender Systems Handbook

    Francesco Ricci, Lior Rokach, and Bracha Shapira (Eds.). 2015.Recommender Systems Handbook. Springer

    work page 2015

  27. [27]

    Markus Schedl. 2016. The LFM-1b Dataset for Music Retrieval and Recommenda- tion. InProceedings of the ACM International Conference on Multimedia Retrieval (ICMR). 103–110. doi:10.1145/2911996.2912004

    work page doi:10.1145/2911996.2912004 2016

  28. [28]

    Nakarin Sritrakool and Saranya Maneeroj. 2021. Personalized Preference Drift Aware Sequential Recommender System.IEEE AccessPP (2021), 1–1. doi:10.1109/ access.2021.3128769

    work page arXiv 2021

  29. [29]

    Adith Swaminathan and Thorsten Joachims. 2017. Counterfactual Evaluation and Learning for Search, Recommendation and Ad Placement. InProceedings of the 26th International World Wide Web Conference (WWW). 1199–1208

    work page 2017

  30. [30]

    John Sweller. 1988. Cognitive Load During Problem Solving: Effects on Learning. Cognitive Science12, 2 (1988), 257–285

    work page 1988

  31. [31]

    María Cora Urdaneta-Ponte, Amaia Méndez-Zorrilla, and Ibon Oleagordia-Ruíz

    work page

  32. [32]

    Lifelong Learning Courses Recommendation System to Improve Profes- sional Skills Using Ontology and Machine Learning.Applied Sciences11 (2021),

    work page 2021

  33. [33]

    doi:10.3390/app11093839

    work page doi:10.3390/app11093839

  34. [34]

    Michal Valko, Nathan Korda, Rémi Munos, Ilias Flaounas, and Nello Cristianini

    work page

  35. [35]

    Finite-time analysis of kernelised contextual bandits. InUAI

    work page

  36. [36]

    Shoujin Wang, Qi Zhang, Liang Hu, Xiuzhen Zhang, Yan Wang, and Charu Aggar- wal. 2022. Sequential/session-based recommendations: Challenges, approaches, applications and opportunities. InProceedings of the 45th international ACM SIGIR conference on research and development in information retrieval. 3425–3428

    work page 2022

  37. [37]

    Haolun Wu, Chen Ma, Bhaskar Mitra, Fernando Diaz, and Xue Liu. 2022. A multi-objective optimization framework for multi-stakeholder fairness-aware recommendation.ACM Transactions on Information Systems41, 2 (2022), 1–29

    work page 2022

  38. [38]

    Cui, Jing Li, Junjie Yao, and Cheng Chen

    Hongzhi Yin, B. Cui, Jing Li, Junjie Yao, and Cheng Chen. 2012. Challenging the Long Tail Recommendation.ArXivabs/1205.6700 (2012). doi:10.14778/2311906. 2311916

    work page doi:10.14778/2311906 2012

  39. [39]

    Hyunsik Yoo, SeongKu Kang, and Hanghang Tong. 2025. Continual Recommender Systems.ArXivabs/2507.03861 (2025). doi:10.48550/arxiv.2507.03861

    work page doi:10.48550/arxiv.2507.03861 2025

  40. [40]

    Qingbo Zhang, Xiaochun Yang, Hao Chen, Bin Wang, Zhu Sun, and Xiangmin Zhou. 2025. Adaptive Intention Learning for Session-Based Recommendation. ACM Transactions on Intelligent Systems and Technology16, 2 (2025), 1–26

    work page 2025

  41. [41]

    Aggarwal, and Tyler Derr

    Yuying Zhao, Yu Wang, Yunchao Liu, Xueqi Cheng, Charu C. Aggarwal, and Tyler Derr. 2024. Fairness and Diversity in Recommender Systems: A Survey. Comput. Surveys56, 3 (2024)

    work page 2024

  42. [42]

    Yunhong Zhou, Dennis Wilkinson, Robert Schreiber, and Rong Pan. 2010. Solving the Cold-Start Problem in Large-Scale Recommendation Systems: A Bandit-Based Approach. InProceedings of the 4th ACM Conference on Recommender Systems (RecSys). 121–128

    work page 2010

  43. [43]

    Ziwei Zhu, Yun He, Xing Zhao, Yin Zhang, Jianling Wang, and James Caverlee

    work page

  44. [44]

    InProceedings of the 14th ACM international conference on web search and data mining

    Popularity-opportunity bias in collaborative filtering. InProceedings of the 14th ACM international conference on web search and data mining. 85–93

    work page