Learning to Retrieve User History and Generate User Profiles for Personalized Persuasiveness Prediction

Jongwon Lim; Sejun Park; Yohan Jo; Yoonah Park

arxiv: 2601.05654 · v3 · submitted 2026-01-09 · 💻 cs.CL · cs.AI

Learning to Retrieve User History and Generate User Profiles for Personalized Persuasiveness Prediction

Sejun Park , Yoonah Park , Jongwon Lim , Yohan Jo This is my paper

Pith reviewed 2026-05-16 16:18 UTC · model grok-4.3

classification 💻 cs.CL cs.AI

keywords persuasiveness predictionuser profilingquery generationpersonalized predictionChangeMyView datasetcontext-aware retrievalhistory summarization

0 comments

The pith

A trainable query generator pulls relevant user history records and a profiler condenses them into context-specific profiles that raise persuasiveness prediction accuracy.

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

The paper sets out to show that persuasiveness predictions become more accurate when models have access to a persuadee's past activities rather than treating every user as generic. It builds a two-part system: one component learns to write queries that surface the history entries most likely to matter for the current message, and the second component turns those entries into a short profile that the main predictor can use. On the ChangeMyView Reddit dataset the approach lifts F1 scores from 33 percent to 47 percent when the downstream model is Llama-3.3-70B-Instruct, and the gains hold across several predictor architectures. The resulting profiles turn out to be shaped by the immediate conversation context and by which predictor is being used, not by fixed user traits. These findings indicate that task-oriented retrieval and summarization of personal history can supply the missing signal for individualized persuasion estimates.

Core claim

We propose a context-aware user profiling framework with two trainable components: a query generator that generates optimal queries to retrieve persuasion-relevant records from a user's history, and a profiler that summarizes these records into a profile to effectively inform the persuasiveness prediction model. Our evaluation on the ChangeMyView Reddit dataset shows consistent improvements over existing methods across multiple predictor models, raising F1 from 33% to 47% on Llama-3.3-70B-Instruct. Further analysis shows that effective user profiles are context-dependent and predictor-specific, rather than relying on static attributes or surface-level similarity.

What carries the argument

The two-component trainable framework in which a query generator produces targeted retrieval queries over user history and a profiler converts the retrieved records into a concise profile that conditions the downstream persuasiveness predictor.

If this is right

Consistent F1 gains appear across multiple downstream predictor models when the profiled history is supplied.
Effective profiles vary with the specific message context and with the identity of the predictor model rather than remaining static.
Task-oriented retrieval of user history outperforms reliance on fixed attributes or simple similarity measures.
The same history records can be summarized differently to suit different prediction heads.

Where Pith is reading between the lines

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

The same query-plus-profile pattern could be tested on other history-rich personalization tasks such as reply generation or content recommendation.
If the profiles remain predictor-specific, systems may need separate profile generators for each downstream model rather than one universal user summary.
Dynamic updating of the profile as new user messages arrive could be examined to keep the signal current without full re-retrieval.

Load-bearing premise

That the queries produced by the generator will reliably surface history records that are genuinely relevant to persuasion and that the resulting profiles add real predictive signal rather than noise or spurious correlations.

What would settle it

An ablation in which the learned query generator is replaced by random or keyword-only retrieval from the same user history, and the F1 score on the ChangeMyView test set falls back to the 33 percent baseline level.

read the original abstract

Estimating the persuasiveness of messages is critical in various applications, from recommender systems to safety assessment of LLMs. While it is imperative to consider the target persuadee's characteristics, such as their values, experiences, and reasoning styles, there is currently no established systematic framework to optimize leveraging a persuadee's past activities (e.g., conversations) to the benefit of a persuasiveness prediction model. To address this problem, we propose a context-aware user profiling framework with two trainable components: a query generator that generates optimal queries to retrieve persuasion-relevant records from a user's history, and a profiler that summarizes these records into a profile to effectively inform the persuasiveness prediction model. Our evaluation on the ChangeMyView Reddit dataset shows consistent improvements over existing methods across multiple predictor models, raising F1 from 33% to 47% on Llama-3.3-70B-Instruct. Further analysis shows that effective user profiles are context-dependent and predictor-specific, rather than relying on static attributes or surface-level similarity. Together, these results highlight the importance of task-oriented, context-dependent user profiling for personalized persuasiveness prediction.

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

A trainable query generator plus profiler turns user history into task-specific profiles and lifts F1 on CMV data, but end-to-end numbers alone leave the retrieval mechanism unverified.

read the letter

The main thing here is a two-component setup that learns to generate queries for pulling relevant records from a user's past posts and then summarizes them into a profile meant to help a downstream model predict how persuasive a message will be to that user. On the ChangeMyView Reddit dataset the approach raises F1 from 33% to 47% on Llama-3.3-70B-Instruct and shows gains across several other predictors as well. The analysis also indicates that the resulting profiles are context-dependent and predictor-specific rather than static attributes or simple similarity matches. That combination of learned retrieval and task-oriented summarization is the concrete step beyond prior work that relied on fixed user features. The evaluation uses a public dataset and reports consistent numerical improvement, which is straightforward to reproduce once code and training details are released. The soft spot is the absence of checks that would confirm the learned queries are actually surfacing persuasion-relevant content. There are no reported retrieval-precision numbers against human-labeled relevant posts, and no ablation that keeps the profiler and history pool fixed while swapping in non-learned or random queries. Without those, the observed lift could stem from the profiler simply compressing history into a shorter prompt or from incidental correlation with user activity volume. The stress-test note flags this gap correctly. This is for researchers building personalized dialogue or recommender systems where individual background matters. It is coherent enough on its own terms to deserve a serious referee, though any review should request the missing intermediate metrics and controls.

Referee Report

3 major / 2 minor

Summary. The paper proposes a context-aware user profiling framework for personalized persuasiveness prediction consisting of two trainable components: a query generator that produces optimal queries to retrieve persuasion-relevant records from a user's history, and a profiler that summarizes those records into a profile to inform a downstream predictor. Evaluation on the ChangeMyView Reddit dataset reports consistent F1 improvements over baselines across multiple predictor models, with the headline result being an increase from 33% to 47% on Llama-3.3-70B-Instruct; further analysis claims that effective profiles are context-dependent and predictor-specific rather than static or surface-similar.

Significance. If the central mechanism is verified, the work would be significant for applications in recommender systems and LLM safety assessment by providing a systematic, trainable approach to leverage user history (values, experiences, reasoning styles) rather than relying on static attributes. The reported numerical lift across base models is a concrete strength, and the emphasis on task-oriented profiling is a useful conceptual contribution; however, the absence of intermediate metrics or controls leaves open whether the gains stem from the intended retrieval mechanism or incidental effects.

major comments (3)

[Evaluation / Results] Evaluation section (results on ChangeMyView): the headline F1 lift from 33% to 47% on Llama-3.3-70B-Instruct is reported only as end-to-end performance across predictors; no retrieval-precision metric against human-labeled relevant posts, no ablation disabling the learned query generator while retaining the same history pool and profiler, and no control feeding random or surface-similar records are provided. This leaves the central claim that the query generator surfaces persuasion-relevant records (rather than prompt-length or summarization effects) unverified and load-bearing for the reported gains.
[Framework / §4] §4 (framework description): the query generator and profiler are described as trainable, yet the manuscript provides no details on the exact training objectives, loss functions, or how supervision is obtained for the query generator on the same domain as the downstream task, raising the risk that reported improvements partly reflect domain-specific overfitting rather than generalizable personalization.
[Results / Tables] Table or results paragraph reporting F1 scores: no statistical significance tests (e.g., paired t-tests or bootstrap confidence intervals) or variance across multiple runs are mentioned, so it is unclear whether the 14-point absolute F1 gain is robust or could be explained by implementation differences in the baseline predictors.

minor comments (2)

[Evaluation] Clarify the exact baseline implementations (e.g., how existing methods handle user history) and whether they were re-implemented with identical prompt formats and history truncation to ensure fair comparison.
[Analysis] The claim that profiles are 'context-dependent and predictor-specific' is supported only by qualitative analysis; adding a quantitative measure (e.g., profile similarity across contexts) would strengthen the supporting argument without altering the main results.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. We address each major comment below and will incorporate revisions to strengthen the evaluation, framework description, and statistical reporting in the manuscript.

read point-by-point responses

Referee: [Evaluation / Results] Evaluation section (results on ChangeMyView): the headline F1 lift from 33% to 47% on Llama-3.3-70B-Instruct is reported only as end-to-end performance across predictors; no retrieval-precision metric against human-labeled relevant posts, no ablation disabling the learned query generator while retaining the same history pool and profiler, and no control feeding random or surface-similar records are provided. This leaves the central claim that the query generator surfaces persuasion-relevant records (rather than prompt-length or summarization effects) unverified and load-bearing for the reported gains.

Authors: We agree that intermediate metrics and controls are needed to isolate the contribution of the learned query generator. In the revised manuscript we will add: (1) a retrieval-precision evaluation on a human-annotated subset of retrieved posts, (2) an ablation that disables the query generator and instead retrieves from the full history pool (or most-recent posts) while keeping the profiler and predictor unchanged, and (3) a surface-similarity control that retrieves records by embedding cosine similarity. These additions will directly test whether the observed gains arise from context-aware retrieval rather than incidental effects. revision: yes
Referee: [Framework / §4] §4 (framework description): the query generator and profiler are described as trainable, yet the manuscript provides no details on the exact training objectives, loss functions, or how supervision is obtained for the query generator on the same domain as the downstream task, raising the risk that reported improvements partly reflect domain-specific overfitting rather than generalizable personalization.

Authors: We will expand Section 4 with a dedicated subsection on training. This will specify the exact objective and loss function used for the query generator, the supervision signal (derived from downstream-task performance on held-out data), and the training procedure for the profiler. The added details will allow readers to assess reproducibility and the extent of domain-specific adaptation. revision: yes
Referee: [Results / Tables] Table or results paragraph reporting F1 scores: no statistical significance tests (e.g., paired t-tests or bootstrap confidence intervals) or variance across multiple runs are mentioned, so it is unclear whether the 14-point absolute F1 gain is robust or could be explained by implementation differences in the baseline predictors.

Authors: We will augment the results section and tables with bootstrap confidence intervals for all F1 scores and paired statistical tests (t-tests or Wilcoxon signed-rank) comparing our method against each baseline. We will also report mean and standard deviation of F1 across five independent runs with different random seeds for every model and configuration, thereby demonstrating that the reported gains are stable. revision: yes

Circularity Check

0 steps flagged

No significant circularity; improvements measured on held-out test data

full rationale

The paper trains a query generator and profiler on the ChangeMyView dataset but reports F1 gains (33% to 47%) on held-out test data across multiple predictor models. No equations, self-citations, or derivations reduce the final metric to a parameter fitted on the same test instances. The central claim therefore remains an empirical measurement against an external benchmark split rather than a tautological restatement of inputs.

Axiom & Free-Parameter Ledger

2 free parameters · 1 axioms · 0 invented entities

The framework rests on the assumption that user history contains extractable persuasion-relevant signals and that learned retrieval plus summarization can surface them without introducing harmful bias. The trainable components introduce many fitted parameters whose values are determined by supervised training on the target dataset.

free parameters (2)

query generator parameters
Weights of the model that learns to produce optimal retrieval queries from the current message and user context.
profiler parameters
Weights of the model that learns to summarize retrieved records into a profile useful for the downstream predictor.

axioms (1)

domain assumption Past user conversations contain information that is predictive of future persuasiveness responses.
Invoked when the method assumes retrieved history will improve prediction accuracy.

pith-pipeline@v0.9.0 · 5500 in / 1391 out tokens · 51274 ms · 2026-05-16T16:18:54.817907+00:00 · methodology

Learning to Retrieve User History and Generate User Profiles for Personalized Persuasiveness Prediction

Core claim

What carries the argument

If this is right

Where Pith is reading between the lines

Load-bearing premise

What would settle it

discussion (0)