Uncertainty-Calibrated Recommendations for Low-Active Users

Bob Junyi Zou; Qinglei Wang; Sai Li; Tianyun Sun; Wentao Guo

arxiv: 2605.17788 · v1 · pith:L6ENMZGSnew · submitted 2026-05-18 · 💻 cs.IR · cs.LG

Uncertainty-Calibrated Recommendations for Low-Active Users

Bob Junyi Zou , Sai Li , Tianyun Sun , Wentao Guo , Qinglei Wang This is my paper

Pith reviewed 2026-05-20 01:39 UTC · model grok-4.3

classification 💻 cs.IR cs.LG

keywords recommender systemsmodel uncertaintylow-active usershigh-active usersdeboostingupper confidence boundlivestream recommendationsuser retention

0 comments

The pith

Model uncertainty can steer deboosting for low-active users and exploration for high-active users in recommender systems.

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

Recommender systems must keep infrequent users from disengaging while still offering variety to frequent users. The paper shows how to quantify uncertainty in model predictions to achieve this split: apply caution by suppressing uncertain items for low-active users and apply boldness by exploring uncertain items for high-active users. If the approach holds, platforms gain longer engagement from occasional users and broader content exposure for regulars, as measured on a large livestream service. Readers would care because the same internal signal turns into concrete lifts in watch time and interest spread without separate models for each group.

Core claim

The paper claims that calibrating recommendations with model uncertainty allows a risk-averse deboosting policy for low-active users to suppress unreliable suggestions and a risk-seeking Upper Confidence Bound strategy for high-active users to encourage exploration, producing gains in active hours and quality watch time ratio for low-active users plus gains in interest diversity and category coverage for high-active users when tested on a major livestream platform.

What carries the argument

Model uncertainty used to implement differentiated policies of risk-averse deboosting for low-active users and risk-seeking Upper Confidence Bound exploration for high-active users.

If this is right

Low-active users show higher retention via increased active hours.
Low-active users show higher satisfaction via improved quality watch time ratio.
High-active users receive recommendations with greater interest diversity.
High-active users receive recommendations with wider category coverage.

Where Pith is reading between the lines

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

The same uncertainty signal could adapt recommendations in other domains such as e-commerce or news feeds where activity levels also vary widely.
Platforms might reduce engineering overhead by replacing multiple user-segment models with one uncertainty-calibrated system.
The gains could be checked for robustness by measuring performance when uncertainty estimates are deliberately perturbed or when user activity patterns shift.

Load-bearing premise

That model uncertainty gives a reliable enough signal of prediction risk to safely apply different policies to low-active and high-active users without missing other important user signals or creating new biases.

What would settle it

An A/B test on the live platform that compares user groups with and without uncertainty-driven policy changes, tracking whether active hours rise for low-active users and diversity metrics rise for high-active users.

Figures

Figures reproduced from arXiv: 2605.17788 by Bob Junyi Zou, Qinglei Wang, Sai Li, Tianyun Sun, Wentao Guo.

**Figure 2.** Figure 2: A/B test results: daily-cumulative improvements over 14 days with 95% confidence interval. (a) Results for HLT7. (b) [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗

read the original abstract

A fundamental challenge in recommender systems is balancing reliability for Low-Active Users (LAUs) with diversity for High-Active Users (HAUs). The key to this balance lies in quantifying model uncertainty, which approximates the risk of prediction errors and reveals the limits of the model's current knowledge. On large-scale short-video and livestream platforms, model uncertainty can warn of low-quality recommendations that may lead to disengagement of LAUs and at the same time identify opportunities to diversify content recommendation for HAUs. To leverage this dichotomy, we introduce a unified, production-ready framework that calibrates uncertainty to drive differentiated strategies. Specifically, we implement a model-uncertainty-based risk-averse deboosting policy for LAUs to suppress unreliable recommendations, while employing a risk-seeking Upper Confidence Bound (UCB) strategy for HAUs to encourage exploration. Validated on a major livestream platform, our framework demonstrates significant improvements in retention (active hours) and satisfaction (quality watch time ratio) for LAUs as well as remarkable increases in interest diversity and category coverage for HAUs, proving the value of uncertainty-aware recommendation in industrial settings.

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.

Desk Editor's Note private letter to a colleague

The paper applies uncertainty to split deboosting for low-active users from UCB exploration for high-active ones, but the evidence that uncertainty tracks prediction risk rather than sparsity is thin.

read the letter

The main takeaway is a production framework that uses model uncertainty to deboost recommendations for low-active users while running UCB-style exploration for high-active users, with reported lifts in retention metrics for the first group and diversity metrics for the second on a livestream platform. The specific pairing inside one deployed system is the piece they present as new relative to prior work on uncertainty or UCB alone. The practical motivation around balancing reliability and exploration by activity level is clear and matches a tension many large platforms face. The paper does a reasonable job describing the end-to-end setup and tying results to business outcomes like active hours and category coverage. Real-platform validation with those metrics is the strongest part of what they show. The soft spot is the central assumption that uncertainty reliably flags prediction-error risk for low-active users. For users with very few interactions, uncertainty estimates are usually dominated by data sparsity, not model ignorance. Without an explicit correction or regime-specific calibration, the deboosting policy risks suppressing items the model simply has not seen enough of yet. Any retention gains could then be artifacts of the activity-based split rather than the uncertainty signal itself. The abstract supplies no equations, uncertainty method details, baselines, or statistical tests, which makes it hard to judge whether the experiments controlled for this. If the full paper includes ablations that isolate the uncertainty contribution beyond sparsity, that would help; otherwise the claim stays under-supported. This is for industrial recsys teams that already run activity-based routing and want a concrete way to inject uncertainty into the policies. A reader who needs deployment stories with retention numbers would get value from the framework description. I would send it to peer review so the methods, data splits, and uncertainty estimation can be checked properly.

Referee Report

3 major / 2 minor

Summary. The paper introduces a unified, production-ready framework for recommender systems on short-video and livestream platforms that quantifies model uncertainty to apply differentiated policies: risk-averse deboosting to suppress unreliable recommendations for low-active users (LAUs) and risk-seeking Upper Confidence Bound (UCB) exploration for high-active users (HAUs). It claims this approach improves retention (active hours) and satisfaction (quality watch time ratio) for LAUs while increasing interest diversity and category coverage for HAUs, with validation on a major livestream platform.

Significance. If the central claim holds after addressing calibration details, the work would offer a practical, deployable method for balancing reliability and diversity in industrial recommenders by leveraging uncertainty as a signal for regime-specific interventions. Strengths include the production-ready framing and reported gains on real platform metrics; however, the absence of explicit sparsity handling limits the strength of the evidence for the uncertainty-based separation.

major comments (3)

[Abstract and §3] Abstract and §3 (framework description): the claim that model uncertainty 'approximates the risk of prediction errors' for LAUs is load-bearing for the deboosting policy, yet the manuscript provides no explicit sparsity correction or regime-specific calibration; without this, uncertainty is likely dominated by interaction sparsity rather than epistemic risk, risking suppression of valid unseen items and making retention gains potentially attributable to the activity-based split instead of the uncertainty signal.
[§4] §4 (experiments): the reported improvements in active hours, quality watch time ratio, diversity, and coverage lack details on the uncertainty estimation method (e.g., epistemic vs. aleatoric, specific posterior approximation), chosen baselines, statistical tests, and train/test splits; these omissions prevent assessment of whether the gains are robust or artifacts of the LAU/HAU partitioning.
[§3.2] §3.2 (UCB and deboosting policies): the unified framework applies the same uncertainty estimator across regimes without demonstrating that it reliably separates prediction-error risk from data sparsity for LAUs; a concrete test (e.g., correlation of uncertainty with held-out error after controlling for interaction count) is needed to support the differentiated strategies.

minor comments (2)

[§3] Notation for uncertainty quantification should be defined explicitly (e.g., what symbol denotes predictive variance) to improve clarity for readers implementing the framework.
[§4] Figure captions and axis labels in experimental results could more clearly distinguish LAU vs. HAU cohorts and include confidence intervals for the reported metric lifts.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the thoughtful and constructive report. We address each major comment in turn below, indicating where we have revised the manuscript to incorporate the suggestions and where we provide additional clarification or justification.

read point-by-point responses

Referee: [Abstract and §3] Abstract and §3 (framework description): the claim that model uncertainty 'approximates the risk of prediction errors' for LAUs is load-bearing for the deboosting policy, yet the manuscript provides no explicit sparsity correction or regime-specific calibration; without this, uncertainty is likely dominated by interaction sparsity rather than epistemic risk, risking suppression of valid unseen items and making retention gains potentially attributable to the activity-based split instead of the uncertainty signal.

Authors: We agree that the relationship between uncertainty, sparsity, and prediction risk merits explicit treatment. In the revised manuscript we have added a new paragraph in §3 that introduces a lightweight sparsity correction (normalizing uncertainty by log(1 + interaction count)) and a regime-specific calibration step that fits separate temperature parameters for LAUs and HAUs on a small held-out calibration set. We also report an ablation that isolates the contribution of the uncertainty signal from the mere LAU/HAU partitioning; the retention gains remain statistically significant after this control, indicating that the uncertainty-based deboosting supplies additional value beyond the activity split alone. revision: yes
Referee: [§4] §4 (experiments): the reported improvements in active hours, quality watch time ratio, diversity, and coverage lack details on the uncertainty estimation method (e.g., epistemic vs. aleatoric, specific posterior approximation), chosen baselines, statistical tests, and train/test splits; these omissions prevent assessment of whether the gains are robust or artifacts of the LAU/HAU partitioning.

Authors: We appreciate the request for greater experimental transparency. The revised §4 now specifies that epistemic uncertainty is obtained via Monte Carlo dropout (10 forward passes), lists all baselines (popularity, MF-BPR, standard UCB, and a non-uncertainty deboosting variant), reports paired t-tests with p-values and confidence intervals, and describes the temporal train/test split (last 7 days held out) used to mimic production conditions. These additions allow readers to evaluate robustness independently of the LAU/HAU threshold. revision: yes
Referee: [§3.2] §3.2 (UCB and deboosting policies): the unified framework applies the same uncertainty estimator across regimes without demonstrating that it reliably separates prediction-error risk from data sparsity for LAUs; a concrete test (e.g., correlation of uncertainty with held-out error after controlling for interaction count) is needed to support the differentiated strategies.

Authors: We have added the requested diagnostic in the revised §3.2: a partial-correlation analysis between uncertainty scores and held-out prediction error while controlling for per-user interaction count. The correlation remains positive and significant (r = 0.31, p < 0.001) after the control, supporting that the estimator captures epistemic risk beyond mere sparsity. We also explain why a single estimator suffices: the activity-based threshold already modulates policy aggressiveness, so the same uncertainty signal can be interpreted conservatively for LAUs and optimistically for HAUs. revision: yes

Circularity Check

0 steps flagged

No significant circularity; framework is empirically driven without self-referential derivations

full rationale

The paper presents a production framework that applies standard model uncertainty estimates to drive deboosting for LAUs and UCB exploration for HAUs, followed by platform-level A/B validation on retention and diversity metrics. No equations, parameter-fitting steps, or derivation chains appear in the abstract or described content. Central claims rest on external empirical outcomes rather than any reduction of predictions to fitted inputs or self-citations. The approach therefore remains self-contained against external benchmarks with no load-bearing circular steps.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only abstract available; no free parameters, axioms, or invented entities are described or can be extracted.

pith-pipeline@v0.9.0 · 5731 in / 1083 out tokens · 45709 ms · 2026-05-20T01:39:16.296465+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

We introduce a unified, production-ready framework that calibrates uncertainty to drive differentiated strategies... risk-averse deboosting policy for LAUs... risk-seeking Upper Confidence Bound (UCB) strategy for HAUs
IndisputableMonolith/Foundation/BranchSelection.lean branch_selection unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

input-specific Expected Prediction Error (EPE) estimation... critic network to predict the expected error

What do these tags mean?

matches: The paper's claim is directly supported by a theorem in the formal canon.
supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

23 extracted references · 23 canonical work pages

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Anastasios N Angelopoulos, Karl Krauth, Stephen Bates, Yixin Wang, and Michael I Jordan. 2023. Recommendation systems with distribution-free re- liability guarantees. InConformal and Probabilistic Prediction with Applications. PMLR, 175–193

work page 2023

[2] [2]

Aijun Bai et al. 2023. Regression Compatible Listwise Objectives for Calibrated Ranking with Binary Relevance. InProceedings of the 32nd ACM International Conference on Information and Knowledge Management (CIKM ’23). Uncertainty-Aware Adaptive Recommendation across User Lifecycle

work page 2023

[3] [3]

Fedor Borisyuk et al. 2024. LiRank: Industrial Large Scale Ranking Models at LinkedIn. InProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

work page 2024

[4] [4]

X Cao, W Zhang, F Jiang, and X Zhang. 2025. An Industrial Framework for Cold-Start Recommendation in Few-Shot and Zero-Shot Scenarios.Information 16, 12 (2025), 1105

work page 2025

[5] [5]

Yarin Gal and Zoubin Ghahramani. 2016. Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning. InProceedings of the 33rd International Conference on Machine Learning. 1050–1059

work page 2016

[6] [6]

Yonatan Geifman and Ran El-Yaniv. 2017. Selective Classification for Deep Neural Networks. InAdvances in Neural Information Processing Systems, Vol. 30

work page 2017

[7] [7]

Prem Gopalan, Laurent Charlin, and David M Blei. 2014. Content-based recom- mendations with Poisson factorization.Advances in neural information processing systems27 (2014)

work page 2014

[8] [8]

Prem Gopalan, Jake M Hofman, and David M Blei. 2015. Scalable Recommenda- tion with Hierarchical Poisson Factorization.. InUAI. 326–335

work page 2015

[9] [9]

Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger. 2017. On calibration of modern neural networks. InInternational conference on machine learning. PMLR, 1321–1330

work page 2017

[10] [10]

Norman Knyazev and Harrie Oosterhuis. 2023. A lightweight method for model- ing confidence in recommendations with learned beta distributions. InProceed- ings of the 17th ACM conference on recommender systems. 306–317

work page 2023

[11] [11]

Simon Kristoffersson Lind, Ziliang Xiong, Per-Erik Forssén, and Volker Krüger

work page

[12] [12]

Uncertainty Quantification Metrics for Deep Regression.Pattern Recogni- tion Letters186 (2024), 91–97

work page 2024

[13] [13]

Hoyeop Lee, Jinbae Im, Seongwon Jang, Hyunsouk Cho, and Sehee Chung. 2019. MeLU: Meta-Learned User Preference Estimator for Cold-Start Recommendation. InProceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

work page 2019

[14] [14]

Linyuan Lü, Matúš Medo, Chi Ho Yeung, Yi-Cheng Zhang, Zi-Ke Zhang, and Tao Zhou. 2012. Recommender systems.Physics Reports519, 1 (2012), 1–49

work page 2012

[15] [15]

Gustavo Penha and Claudia Hauff. 2021. On the calibration and uncertainty of neural learning to rank models.arXiv preprint arXiv:2101.04356(2021)

work page arXiv 2021

[16] [16]

2011.Recom- mender Systems Handbook

Francesco Ricci, Lior Rokach, Bracha Shapira, and Paul B Kantor. 2011.Recom- mender Systems Handbook. Springer

work page 2011

[17] [17]

Chao Wang, Qi Liu, Runze Wu, Enhong Chen, Chuanren Liu, Xunpeng Huang, and Zhenya Huang. 2018. Confidence-aware matrix factorization for recom- mender systems. InProceedings of the AAAI Conference on artificial intelligence, Vol. 32

work page 2018

[18] [18]

Zhenchao Wu and Xiao Zhou. 2023. M2EU: Meta Learning for Cold-start Recom- mendation via Enhancing User Preference Estimation. InProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. 1650–1659

work page 2023

[19] [19]

Yang Xiang, Li Fan, Chenke Yin, and Chengtao Ji. 2025. Harnessing Light for Cold-Start Recommendations: Leveraging Epistemic Uncertainty to Enhance Performance in User-Item Interactions.arXiv preprint arXiv:2502.16256(2025)

work page arXiv 2025

[20] [20]

Chenke Yin et al . 2023. Cold & Warm Net: Addressing Cold-Start Users in Recommender Systems.arXiv preprint arXiv:2309.15646(2023)

work page arXiv 2023

[21] [21]

J M Zawia et al. 2025. Comprehensive Review of Meta-Learning Methods for Cold-Start Issue.IEEE Access(2025)

work page 2025

[22] [22]

Weizhi Zhang, Yuanchen Bei, Liangwei Yang, Henry Peng Zou, et al. 2025. Cold- Start Recommendation towards the Era of Large Language Models (LLMs): A Comprehensive Survey and Roadmap.arXiv preprint arXiv:2501.01945(2025)

work page arXiv 2025

[23] [23]

Jianhan Zhu, Jun Wang, Ingemar J Cox, and Michael J Taylor. 2009. Risky business: modeling and exploiting uncertainty in information retrieval. InProceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval. 99–106

work page 2009