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arxiv: 2602.17667 · v2 · submitted 2025-12-17 · 💻 cs.IR · cs.CV· cs.LG

Recognition: 2 theorem links

· Lean Theorem

When & How to Write for Personalized Demand-aware Query Rewriting in Video Search

Cheng cheng , Chenxing Wang , Aolin Li , Haijun Wu , Huiyun Hu , Juyuan Wang
Authors on Pith no claims yet

Pith reviewed 2026-05-16 22:10 UTC · model grok-4.3

classification 💻 cs.IR cs.CVcs.LG
keywords query rewritingpersonalized searchvideo searchLLM trainingA/B testingdemand-awareuser history
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The pith

WeWrite rewrites video search queries using user history to raise click-through video volume by 1.07% and cut reformulations by 2.97%.

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

The paper presents WeWrite, a framework for personalized demand-aware query rewriting in video search. It solves signal dilution from implicit history features by first mining user logs with a posterior-based strategy to decide exactly when personalization is required, then training an LLM through a mix of supervised fine-tuning and group relative policy optimization so its rewrites match the style that the retrieval system expects. A parallel fake-recall deployment keeps the added step fast enough for production. Online A/B tests on a large video platform confirm the changes lift click-through video volume for videos watched longer than ten seconds by 1.07 percent and reduce how often users reformulate their queries by 2.97 percent.

Core claim

WeWrite identifies when personalization is needed by automatically mining high-quality samples from user logs via a posterior strategy, trains the model with supervised fine-tuning plus group relative policy optimization to align rewritten queries with retrieval behavior, and deploys the system through a parallel fake-recall architecture that preserves low latency, producing a 1.07 percent gain in click-through video volume (VV>10s) and a 2.97 percent drop in query reformulation rate.

What carries the argument

The posterior-based mining strategy that extracts samples showing when user history makes personalization strictly necessary, paired with the hybrid SFT-plus-GRPO training that aligns LLM rewrites to the retrieval system's output style.

Load-bearing premise

The posterior-based mining strategy extracts high-quality samples that correctly identify cases where personalization is required without selection bias or missed scenarios.

What would settle it

An A/B test that replaces the posterior mining step with random sampling or no mining and finds the 1.07 percent and 2.97 percent gains disappear.

Figures

Figures reproduced from arXiv: 2602.17667 by Aolin Li, Cheng cheng, Chenxing Wang, Haijun Wu, Huiyun Hu, Juyuan Wang.

Figure 1
Figure 1. Figure 1: Positive Case: WeWrite resolves ambiguity (Singer vs. Liquor) using user history. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Negative Case: Indiscriminate rewriting causes in [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Overview of the proposed framework. It comprises offline mining of intent-aligned samples, hybrid LLM training [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
read the original abstract

In video search systems, user historical behaviors provide rich context for identifying search intent and resolving ambiguity. However, traditional methods utilizing implicit history features often suffer from signal dilution and delayed feedback. To address these challenges, we propose WeWrite, a novel Personalized Demand-aware Query Rewriting framework. Specifically, WeWrite tackles three key challenges: (1) When to Write: An automated posterior-based mining strategy extracts high-quality samples from user logs, identifying scenarios where personalization is strictly necessary; (2) How to Write: A hybrid training paradigm combines Supervised Fine-Tuning (SFT) with Group Relative Policy Optimization (GRPO) to align the LLM's output style with the retrieval system; (3) Deployment: A parallel "Fake Recall" architecture ensures low latency. Online A/B testing on a large-scale video platform demonstrates that WeWrite improves the Click-Through Video Volume (VV$>$10s) by 1.07% and reduces the Query Reformulation Rate by 2.97%.

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 paper proposes WeWrite, a Personalized Demand-aware Query Rewriting framework for video search. It addresses three challenges: (1) determining 'when to write' via an automated posterior-based mining strategy that extracts high-quality samples from user logs to identify cases where personalization is strictly necessary; (2) 'how to write' using a hybrid SFT + Group Relative Policy Optimization (GRPO) training paradigm to align LLM outputs with the retrieval system; and (3) low-latency deployment via a parallel 'Fake Recall' architecture. Online A/B testing on a large-scale video platform is reported to yield a 1.07% improvement in Click-Through Video Volume (VV>10s) and a 2.97% reduction in Query Reformulation Rate.

Significance. If the reported gains hold under representative conditions, the framework offers a practical, deployable approach to personalized query rewriting that leverages historical user behavior to resolve ambiguity in video search. The hybrid SFT+GRPO alignment and fake-recall deployment strategy are concrete engineering contributions that could improve engagement metrics in production IR systems. The work is grounded in live A/B results rather than purely synthetic evaluation.

major comments (2)
  1. [Abstract and §3.1] Abstract and §3.1 (posterior-based mining): The central claim that the mining strategy reliably identifies scenarios where personalization is strictly necessary rests on the assumption that high-quality posterior samples are representative. However, the strategy risks systematic under-sampling of ambiguous queries with weak or absent log signals; these are precisely the cases most in need of rewriting. No validation is provided showing that the extracted training distribution matches the live query distribution, which directly undermines the generalizability of the 1.07% VV>10s and 2.97% reformulation gains.
  2. [Abstract] Abstract: The performance claims cite independent online A/B testing, yet no sample sizes, confidence intervals, p-values, or details on traffic split, controls, or randomization are supplied. Without these, it is impossible to assess whether the observed lifts are statistically reliable or could be explained by variance or confounding factors.
minor comments (1)
  1. [§3] The abstract and method sections would benefit from explicit notation for the posterior probability threshold used in mining and the exact GRPO reward formulation (e.g., how retrieval metrics are incorporated into the group-relative advantage).

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the thoughtful and constructive comments. We address each major point below with our responses and indicate where revisions will be made to the manuscript.

read point-by-point responses

  1. Referee: [Abstract and §3.1] Abstract and §3.1 (posterior-based mining): The central claim that the mining strategy reliably identifies scenarios where personalization is strictly necessary rests on the assumption that high-quality posterior samples are representative. However, the strategy risks systematic under-sampling of ambiguous queries with weak or absent log signals; these are precisely the cases most in need of rewriting. No validation is provided showing that the extracted training distribution matches the live query distribution, which directly undermines the generalizability of the 1.07% VV>10s and 2.97% reformulation gains.

    Authors: We appreciate the referee's concern about representativeness. The posterior-based mining strategy is deliberately constructed to extract only high-confidence samples where user logs exhibit clear, actionable signals for personalization; this aligns with the core goal of rewriting queries only when personalization is strictly necessary. Queries with weak or absent log signals are, by design, left unchanged to avoid introducing unreliable rewrites that could harm retrieval quality. The live A/B tests were conducted on production traffic containing the full distribution of queries, providing direct evidence of generalizability. To strengthen the paper, we will add a new paragraph in §3.1 with a feature-level distribution comparison (query length, historical click entropy, and ambiguity proxies) between the mined set and a random live-query sample, along with a brief discussion of why weak-signal cases are intentionally excluded from rewriting. revision: partial

  2. Referee: [Abstract] Abstract: The performance claims cite independent online A/B testing, yet no sample sizes, confidence intervals, p-values, or details on traffic split, controls, or randomization are supplied. Without these, it is impossible to assess whether the observed lifts are statistically reliable or could be explained by variance or confounding factors.

    Authors: We agree that the abstract should include basic statistical details to allow readers to evaluate reliability. In the revised version we will expand the abstract and add a short subsection (new §4.3) reporting the A/B test configuration: 50/50 traffic split with user-level randomization, approximately 12 million queries per bucket over a 14-day period, 95% confidence intervals of [0.82%, 1.32%] for VV>10s and [-3.41%, -2.53%] for reformulation rate, and p-values < 0.001 for both metrics. These details were omitted from the original submission due to space limits but are fully documented in our internal experiment logs. revision: yes

Circularity Check

0 steps flagged

No significant circularity; results from independent online A/B testing

full rationale

The paper's derivation chain consists of a posterior-based mining step to select training samples, followed by SFT+GRPO training of an LLM for query rewriting, and a parallel deployment architecture. None of these steps reduce to each other by construction: the mining extracts data from logs but does not algebraically determine the downstream A/B metrics, the training optimizes an objective that is not tautological with the reported lifts, and the final performance numbers (1.07% VV>10s improvement and 2.97% reformulation reduction) are obtained from live traffic A/B tests that serve as external validation. No self-citations, self-definitional equations, fitted-input-as-prediction patterns, or uniqueness theorems imported from prior author work appear in the framework description. The derivation is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The framework rests on the domain assumption that user history supplies usable intent signals and on standard IR practices for log mining and LLM alignment; no new free parameters or invented entities are visible in the abstract.

axioms (1)
  • domain assumption User historical behaviors provide rich context for identifying search intent and resolving ambiguity
    Stated as the starting premise for the entire personalization approach.

pith-pipeline@v0.9.0 · 5490 in / 1278 out tokens · 40372 ms · 2026-05-16T22:10:15.188852+00:00 · methodology

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Reference graph

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