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arxiv: 2512.13726 · v1 · submitted 2025-12-13 · 💻 cs.LG · cs.AI

Time-Constrained Recommendations: Reinforcement Learning Strategies for E-Commerce

Sayak Chakrabarty , Souradip Pal This is my paper

Pith reviewed 2026-05-16 22:20 UTC · model grok-4.3

classification 💻 cs.LG cs.AI
keywords reinforcement learningtime-constrained recommendationsslate optimizatione-commerce re-rankingMarkov Decision Processescontextual banditsuser time budgetsbudget-aware utilities
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The pith

Reinforcement learning policies outperform contextual bandits for e-commerce recommendations under tight user time budgets.

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

The paper shows that e-commerce recommenders must balance item relevance against the time users spend evaluating items while scrolling through slates, a constraint ignored by standard methods. It formulates the task as Markov Decision Processes that include budget-aware utilities, allowing agents to learn both preferences and evaluation costs at once. Simulations on re-ranking data demonstrate that on-policy and off-policy reinforcement learning control produce higher engagement than contextual bandit baselines when time resources are limited. A reader would care because mobile shopping interfaces impose real scrolling limits, and policies that respect those limits can deliver more clicks without exhausting user attention.

Core claim

By casting time-constrained slate recommendation as Markov Decision Processes with budget-aware utilities and testing on a simulation built from re-ranking data, the authors find that on-policy and off-policy reinforcement learning control improve performance under tight time budgets relative to contextual bandit methods.

What carries the argument

Markov Decision Processes equipped with budget-aware utilities that treat sequential slate selection as actions whose rewards incorporate both relevance and per-item evaluation cost.

If this is right

  • Policies learn to avoid high-cost items that exceed remaining user time, increasing the fraction of recommendations that receive clicks.
  • Both on-policy methods such as policy gradients and off-policy methods such as Q-learning yield measurable gains when budgets are tight.
  • The MDP formulation unifies preference learning with cost awareness, supporting sequential optimization across multiple slates.
  • The simulation framework permits controlled study of policy behavior on re-ranking data without requiring live user traffic.

Where Pith is reading between the lines

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

  • The same budget-aware MDP approach could be applied to other scrolling interfaces such as news or video feeds where evaluation cost also limits total consumption.
  • Production systems might replace bandit layers with these policies once cost estimates are learned from logged interaction times rather than simulated values.
  • Varying time budgets across users could be handled by conditioning the MDP state on observed scroll speed or session length.
  • If the gains hold in live traffic, platforms would gain a practical reason to move beyond contextual bandits for any interface that imposes hard attention limits.

Load-bearing premise

The simulation framework and re-ranking dataset accurately reflect real user time budgets and the costs of evaluating items within slates.

What would settle it

A live A/B test on an e-commerce platform that measures click-through rate and total engagement time for the learned policies versus bandit baselines, using observed scrolling patterns to define time budgets, would show no statistically significant improvement.

Figures

Figures reproduced from arXiv: 2512.13726 by Sayak Chakrabarty, Souradip Pal.

Figure 1
Figure 1. Figure 1: Slate Recommender System The main aim of slate recommenders is to insert the most relevant element at slot k in the slate where an item relevance scorer generates relevance scores of the available N items and using the slate constructed so far as additional context. The scores are then passed through a sampler to select an item from the available items as shown in [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of reinforcement learning simulation workflow for slate recommendation along with architecture of [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Plots showing variation of Play Rate with discount factor ( [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Plots showing variation of Effective Slate Size with discount factor ( [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Plots showing variation of delta Play Rate & delta Effective Slate Size between [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
read the original abstract

Unlike traditional recommendation tasks, finite user time budgets introduce a critical resource constraint, requiring the recommender system to balance item relevance and evaluation cost. For example, in a mobile shopping interface, users interact with recommendations by scrolling, where each scroll triggers a list of items called slate. Users incur an evaluation cost - time spent assessing item features before deciding to click. Highly relevant items having higher evaluation costs may not fit within the user's time budget, affecting engagement. In this position paper, our objective is to evaluate reinforcement learning algorithms that learn patterns in user preferences and time budgets simultaneously, crafting recommendations with higher engagement potential under resource constraints. Our experiments explore the use of reinforcement learning to recommend items for users using Alibaba's Personalized Re-ranking dataset supporting slate optimization in e-commerce contexts. Our contributions include (i) a unified formulation of time-constrained slate recommendation modeled as Markov Decision Processes (MDPs) with budget-aware utilities; (ii) a simulation framework to study policy behavior on re-ranking data; and (iii) empirical evidence that on-policy and off-policy control can improve performance under tight time budgets than traditional contextual bandit-based methods.

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

2 major / 1 minor

Summary. The manuscript is a position paper that formulates time-constrained slate recommendation as a Markov Decision Process (MDP) with budget-aware utilities, introduces a simulation framework built on Alibaba's Personalized Re-ranking dataset, and reports empirical results claiming that on-policy and off-policy RL methods outperform contextual bandit baselines when user time budgets are tight.

Significance. If the simulation accurately reflects real user scrolling costs and slate dynamics, the unified MDP formulation could meaningfully extend reinforcement learning applications in e-commerce by explicitly trading off relevance against evaluation cost. The work highlights a practical constraint often ignored in standard recsys benchmarks and provides a reusable simulation setup for future study. However, the absence of external validation or detailed metric reporting currently limits the strength of the claimed performance gains.

major comments (2)
  1. [Simulation Framework] Simulation framework section: the paper does not specify whether per-item evaluation costs are estimated from logged scroll/dwell times or assigned as fixed/synthetic values. Because the central claim is that RL improves engagement under tight budgets precisely by respecting cumulative costs, this modeling choice is load-bearing and must be documented with explicit equations or pseudocode for cost generation.
  2. [Experiments] Experiments section: no performance metrics (e.g., click-through rate, cumulative reward, budget utilization), statistical tests, or baseline implementation details (hyperparameters, feature representations for contextual bandits) are reported. Without these, the empirical comparison to bandits cannot be evaluated and the claim that RL is superior under tight budgets remains unsubstantiated.
minor comments (1)
  1. [Abstract] Abstract: the phrasing 'supporting slate optimization in e-commerce contexts' is vague; explicitly link it to the three listed contributions.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our position paper. The comments identify key areas where additional documentation and reporting will strengthen the manuscript, and we address each point below with plans for revision.

read point-by-point responses

  1. Referee: [Simulation Framework] Simulation framework section: the paper does not specify whether per-item evaluation costs are estimated from logged scroll/dwell times or assigned as fixed/synthetic values. Because the central claim is that RL improves engagement under tight budgets precisely by respecting cumulative costs, this modeling choice is load-bearing and must be documented with explicit equations or pseudocode for cost generation.

    Authors: We agree that the simulation framework section requires explicit documentation of the cost modeling process. The revised manuscript will add a new subsection with equations defining per-item evaluation costs as functions of logged scroll and dwell times from the Alibaba Personalized Re-ranking dataset, along with pseudocode for the cumulative cost computation and budget-aware utility calculation. This will clarify that costs are derived from real user interaction logs rather than fixed or purely synthetic values. revision: yes

  2. Referee: [Experiments] Experiments section: no performance metrics (e.g., click-through rate, cumulative reward, budget utilization), statistical tests, or baseline implementation details (hyperparameters, feature representations for contextual bandits) are reported. Without these, the empirical comparison to bandits cannot be evaluated and the claim that RL is superior under tight budgets remains unsubstantiated.

    Authors: We acknowledge that the current experimental reporting is insufficient to fully substantiate the performance claims. In the revised version, we will expand the Experiments section to include tables with click-through rates, cumulative rewards, and budget utilization metrics across varying time budget levels. We will also report hyperparameter settings for the on-policy and off-policy RL methods, feature representations used for the contextual bandit baselines, and results of statistical significance tests (e.g., paired t-tests with p-values) comparing RL policies to the bandit baselines under tight budgets. revision: yes

Circularity Check

0 steps flagged

No significant circularity; formulation and results rest on external dataset and standard MDP concepts

full rationale

The paper models time-constrained slate recommendation as an MDP with budget-aware utilities and evaluates on-policy/off-policy RL versus contextual bandits via simulation on Alibaba's public Personalized Re-ranking dataset. No equations or claims reduce by construction to self-fitted parameters, self-citations, or renamed inputs; the simulation is presented as an experimental tool rather than a source of definitional predictions. The empirical claim of improved performance under tight budgets is therefore not forced by the paper's own inputs.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The central claim rests on the assumption that user time budgets and evaluation costs can be reliably inferred or simulated from re-ranking interaction data without additional validation.

free parameters (1)
  • time budget parameters
    User time budgets are incorporated into the MDP utility but specific values or distributions are not detailed in the abstract.
axioms (1)
  • domain assumption User evaluation costs for items can be modeled from interaction patterns in the dataset
    Invoked in the budget-aware utility definition for the MDP formulation.

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Forward citations

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Reference graph

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