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arxiv: 2605.27704 · v1 · pith:3K6IWPJ4new · submitted 2026-05-26 · 💻 cs.IR

Joint Optimization of Relevance and Engagement in Multi-Task Ranking for E-Commerce with Efficient LLM Supervision

Luming Chen , Jiaqi Xi , Raghav Saboo , Kenny Chi , Martin Wang , Sudeep Das , Danny Nightingale , Aditya Dodda
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Elyse Winer Akshad Viswanathan
This is my paper

Pith reviewed 2026-06-29 15:11 UTC · model grok-4.3

classification 💻 cs.IR
keywords multi-task rankingordinal relevanceLLM supervisione-commerce searchrelevance-engagement trade-offsemantic alignmentvalue modelcumulative probability
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The pith

A multi-task ranking system uses an ordinal relevance head and LLM-generated labels to enable controllable trade-offs between semantic quality and engagement in e-commerce search.

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

The paper presents a production-scale multi-task ranking system that treats semantic relevance as a primary optimization objective rather than optimizing solely for engagement signals. It introduces an ordinal relevance head that predicts cumulative probabilities over relevance thresholds and integrates these outputs with engagement heads through a unified value model scoring function. Fine-tuned lightweight LLMs supply three-level ordinal labels (irrelevant, moderately relevant, highly relevant) for over 100 million query-item pairs, with measures to maintain alignment to human judgments. Offline metrics such as NDCG@10 and online A/B experiments show improved semantic alignment while core engagement objectives remain intact. The approach addresses biases that arise when ranking favors popular or price-anchored items over items that match user intent.

Core claim

The architecture employs an ordinal relevance head that predicts cumulative probabilities over relevance thresholds, preserving the inherent ordering of labels. These outputs are integrated with engagement heads through a unified value model scoring function, enabling systematic balancing of semantic quality and short-term behavioral signals. Fine-tuned lightweight LLMs generate three-level ordinal relevance labels to provide high-quality supervision at scale while addressing label distribution sensitivity.

What carries the argument

The ordinal relevance head that predicts cumulative probabilities over relevance thresholds, integrated with engagement heads via a unified value model scoring function.

Load-bearing premise

Fine-tuned lightweight LLMs can generate three-level ordinal relevance labels at scale with high alignment to human annotations and without introducing label distribution sensitivity that would undermine the multi-task optimization.

What would settle it

A large-scale human annotation study on a held-out sample of query-item pairs showing low agreement with the LLM labels, followed by retraining that produces no gain or a loss in NDCG@10 and online engagement metrics.

Figures

Figures reproduced from arXiv: 2605.27704 by Aditya Dodda, Akshad Viswanathan, Danny Nightingale, Elyse Winer, Jiaqi Xi, Kenny Chi, Luming Chen, Martin Wang, Raghav Saboo, Sudeep Das.

Figure 1
Figure 1. Figure 1: Multi-task ranking architecture with unified [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Relevance vs. conversion trade-off (NDCG@10) for [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
read the original abstract

Optimizing industrial search ranking models solely for user engagement signals often introduces systematic biases, prioritizing popular or price-anchored items that may not satisfy semantic intent. We present a production-scale multi-task ranking system that integrates semantic relevance as a primary optimization objective, enabling explicit and controllable relevance-engagement trade-offs. Our architecture employs an ordinal relevance head that predicts cumulative probabilities over relevance thresholds, preserving the inherent ordering of labels. These outputs are integrated with engagement heads through a unified value model scoring function, enabling systematic balancing of semantic quality and short-term behavioral signals. To provide high-quality supervision for this multi-task framework, we utilize fine-tuned lightweight Large Language Models (LLMs) to generate three-level ordinal relevance labels: irrelevant, moderately relevant, and highly relevant. We address challenges regarding label distribution sensitivity and ensure high alignment with human annotations to enable efficient labeling for over 100 million query-item pairs. Evaluation across offline metrics, including NDCG@10, and online A/B experiments demonstrates that our approach significantly improves semantic alignment while preserving core engagement objectives.

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 / 0 minor

Summary. The paper claims to present a production-scale multi-task ranking architecture for e-commerce search that adds an ordinal relevance head (predicting cumulative probabilities over three-level labels) to engagement heads via a unified value model scoring function. Fine-tuned lightweight LLMs are used to label >100M query-item pairs with irrelevant/moderately/highly relevant labels; the system is reported to deliver NDCG@10 gains offline and positive A/B results while allowing controllable relevance-engagement trade-offs.

Significance. If the central claims hold, the work would be significant for industrial IR systems by demonstrating a practical way to inject semantic relevance signals at scale without sacrificing engagement metrics. The use of ordinal heads and a unified scorer to manage trade-offs, combined with LLM supervision for massive labeling, addresses a common production pain point.

major comments (2)
  1. [Abstract] Abstract: the claim that the fine-tuned LLMs achieve 'high alignment with human annotations' for the three-level ordinal labels is unsupported by any reported agreement metrics (kappa, accuracy, or confusion matrices versus human raters). This is load-bearing for the multi-task claims because the NDCG@10 and A/B gains are attributed to the relevance head; without label-quality evidence or noise ablations, attribution to the ordinal component versus other modeling choices cannot be assessed.
  2. [Abstract] Abstract: no description is given of how the cumulative-probability outputs of the ordinal head are combined with the engagement heads inside the unified value model, nor of any sensitivity analysis under the reported label distributions. This prevents verification that the architecture actually enables the claimed 'systematic balancing' rather than being dominated by engagement signals.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We respond to each major comment below and indicate planned changes to the manuscript.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the claim that the fine-tuned LLMs achieve 'high alignment with human annotations' for the three-level ordinal labels is unsupported by any reported agreement metrics (kappa, accuracy, or confusion matrices versus human raters). This is load-bearing for the multi-task claims because the NDCG@10 and A/B gains are attributed to the relevance head; without label-quality evidence or noise ablations, attribution to the ordinal component versus other modeling choices cannot be assessed.

    Authors: We agree that the abstract does not report quantitative agreement metrics to support the alignment claim. The manuscript body contains validation details on label quality, but the abstract would be strengthened by including them. We will revise the abstract to report key metrics (e.g., accuracy and Cohen's kappa versus human raters) and note any label-noise considerations to better support attribution of the observed gains. revision: yes

  2. Referee: [Abstract] Abstract: no description is given of how the cumulative-probability outputs of the ordinal head are combined with the engagement heads inside the unified value model, nor of any sensitivity analysis under the reported label distributions. This prevents verification that the architecture actually enables the claimed 'systematic balancing' rather than being dominated by engagement signals.

    Authors: The abstract describes the integration at a high level via the unified value model but does not detail the combination mechanism or sensitivity analysis. We will revise the abstract to briefly explain how the ordinal cumulative probabilities are converted to a relevance score and combined with engagement predictions in the scoring function, and we will reference the sensitivity analysis performed across label distributions (with full details retained in the methods section). revision: yes

Circularity Check

0 steps flagged

No significant circularity; claims rest on external evaluation

full rationale

The provided abstract and context describe a multi-task architecture using LLM-generated ordinal labels, integrated via a unified scoring function, with evaluation on NDCG@10 and A/B tests. No equations, self-citations, or derivations are exhibited that reduce any claimed result to a fitted input or self-definition by construction. The central claims depend on external human alignment and online experiments rather than internal redefinitions, satisfying the criteria for a self-contained derivation chain.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract supplies no information on free parameters, background axioms, or new postulated entities.

pith-pipeline@v0.9.1-grok · 5745 in / 1131 out tokens · 26189 ms · 2026-06-29T15:11:34.920781+00:00 · methodology

discussion (0)

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Reference graph

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