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Reasoning with Evidence, Not Merely Rationales: Verifiable Preference Proofs for LLM-Based Recommendation

Yu Hou, Nathaniel Kang, Pengkai Wang, Hua Li

arXiv:2610.02968v1cs.AI

TL;DR

LLM recommendation rationales may be weakly supported by their selected evidence or have little effect on rankings. PROVE-Rec links preference claims to evidence, gates recommendation through the proof, and tests both dependencies; it improves recommendation performance and evidence grounding and influence on review-rich benchmarks.

  • Problem

    LLM recommendation lacks joint verification that selected evidence supports a preference claim and that the claim affects the target ranking.

    Source: §1 · p. 2

  • Method

    PROVE-Rec generates evidence-linked positive and avoidance claims, recommends using only the proof and selected entries, and tests grounding and ranking influence with interventions.

    Source: §1 · p. 2

  • Results

    Across three review-rich benchmark datasets, PROVE-Rec improves recommendation performance and grounding and influence; gains over strongest baselines range from 2.01% to 7.45%.

    Source: §5.3 · p. 7, §6 · p. 10

  • Takeaways & Limitations

    Linking claims to historical evidence can improve transparency and auditability, though preference proofs reflect model reasoning rather than definitive user intent.

    Source: §6 · p. 11

    Inferred preferences may expose sensitive information or encourage over-trust in explanations, so deployment calls for privacy safeguards and clear communication.

    Source: §6 · p. 11

Abstract

from arXiv · show

Large language models (LLMs) can infer user preferences from interaction histories and reviews, yet the rationales they generate may not reflect the information actually used for recommendation. A preference claim may be weakly supported by its selected evidence, or may have little effect on the final ranking. We refer to these two failures as the grounding-influence gap. We introduce PROVE-REC, a general framework for verifiable preference reasoning in LLM-based recommendation. Pass A converts the complete pre-target history into a compact preference proof consisting of positive and avoidance claims linked to selected evidence entries. Pass B predicts the next item using only the proof and its selected evidence, preventing the recommender from bypassing the reasoning path. To verify evidence-to-proof grounding, we compare the effect of masking selected evidence with masking a comparable control entry. To verify proof-to-recommendation influence, we remove a preference claim and measure the resulting decrease in the target item's ranking margin. A ranking-preservation objective further retains useful information from the complete history. Comprehensive experiments on wide-ranging real-world datasets demonstrate that PROVE-REC consistently outperforms strong sequential, generative, and LLM-enhanced baselines, with improvements of up to 7.45%. Controlled ablations confirm the effectiveness of the two-pass architecture and verification objectives. Moreover, PROVE-REC produces claims that are more strongly grounded in historical evidence and more influential to recommendation while preserving ranking quality.

1 Introduction

LLM recommendation can generate plausible preference rationales without showing that cited evidence supports the claims or that the claims affect rankings. PROVE-Rec makes both links explicit and tests them while preserving recommendation utility.

1 Introduction

  • The grounding–influence gap is that preference claims may lack support from their selected evidence or have little effect on the recommendation, dependencies existing objectives do not jointly verify.Historical reviews can distinguish valued or rejected aspects from item-consumption patterns.Source: §1 · pp. 1–2
  • PROVE-Rec represents positive and avoidance preferences as evidence-linked claims, then restricts recommendation to the proof and its selected history entries.A ranking-preservation objective keeps the proof-gated ranking distribution close to that from the complete history.Source: §1 · p. 2
  • Evidence masking tests whether selected entries support claims, while claim removal tests whether each claim affects the target item's ranking margin.The method compares selected evidence with a comparable control entry and holds selected evidence fixed when removing a claim.Source: §1 · p. 2
  • The framework is not tied to a particular item representation, although experiments instantiate it on a strong generative recommender.The experimental implementation does not define the framework's scope.Source: §1 · p. 2
  • The paper also reports theoretical bounds on grounding and influence failures and conditions for preserving the full-history recommender's top-K ranking.These are summarized among the paper's contributions.Source: §1 · p. 2

2 Related Work

Prior work uses LLMs, item representations, reviews, and intermediate reasoning to improve recommendation, but does not jointly test evidence support and claim-level ranking influence. PROVE-Rec applies paired interventions to those dependencies.

2.1 LLM-Based Recommendation

  • LLM recommendation methods combine language knowledge and collaborative signals, but primarily target recommendation quality or model adaptation rather than whether claims depend on evidence or affect rankings.The related work includes shared sequence-to-sequence models, parameter-efficient tuning, and retrieval-augmented approaches.Source: §2.1 · p. 2

2.2 Generative Recommendation and Item Tokenization

  • Generative recommendation research develops item tokenizations, while PROVE-Rec leaves the item representation unchanged and changes how evidence and preference claims inform prediction.The contrast is about the information path, not a new item representation.Source: §2.2 · p. 3

2.3 Review-Aware and Reasoning-Enhanced Recommendation

  • Review-aware and reasoning-enhanced recommenders use reviews or intermediate reasoning, but accuracy or trajectory objectives do not reveal whether individual claims depend on evidence or affect a ranking.PROVE-Rec focuses on evidence-to-proof grounding and proof-to-recommendation influence.Source: §2.3 · p. 3

2.4 Rationale Grounding and Intervention-Based Evaluation

  • Prior rationale and intervention work motivates perturbation tests, but does not jointly address recommendation-specific evidence selection, preference polarity, claim-level ranking effects, and utility preservation.PROVE-Rec compares masking linked evidence with a control and removing a claim while holding selected evidence fixed.Source: §2.4 · p. 3

3 Preliminaries

The task represents a user's pre-target interactions as an ordered evidence history and the next item as the prediction target. A preference proof is a budgeted set of polarity-marked claims linked to cited history entries.

3.1 Task and Evidence History

  • The evidence history contains interactions observed before the target, ordered chronologically; the target item and future interactions are excluded.Entries include an item, its rating when available, review text, and category metadata.Source: §3.1 · p. 3
  • An LLM recommender represents each item with an output token sequence that may be a textual identifier, learned item sequence, or semantic code.The item score is defined under a context using model parameters and teacher forcing.Source: §3.1 · p. 3

3.2 Preference Proof

  • A preference proof is a compact set of claims with positive or avoidance polarity, a normalized preference aspect, and indices of cited evidence entries.Selected entries are those cited by the proof; unselected entries are not assumed irrelevant and can serve as grounding controls.Source: §3.2 · pp. 3–4
  • Claim and evidence budgets limit proof size; histories with no unselected entries contribute no grounding term because there is no control entry.The data-validation filter enforces the same claim and evidence budgets.Source: §3.2 · p. 4

4 Methodology

PROVE-Rec uses one LLM in two passes: it induces a structured proof from pre-target history, then recommends from the proof and selected evidence only. Training tests both links and preserves full-history ranking information.

4.1 Method Overview

  • Pass A creates a preference proof from the history, and Pass B predicts the next item using only that proof and its selected entries.In the running example, the proof records comfort and avoidance of short battery life, and Pass B recommends a comfortable, long-battery headset.Source: §4.1 · p. 4

4.2 Preference-Proof Induction

  • Proof induction generates positive and avoidance claims in the same format, with polarity distinguishing them; low-rated reviews can support avoidance claims.Offline proofs use only pre-target history and undergo checks for format, evidence identifiers, polarity, budgets, and temporal leakage.Source: §4.2 · p. 4

4.3 Proof-Gated Recommendation

  • Pass B receives only the proof-gated context, excluding the rest of the history, and predicts items through constrained generation.Training checks claim influence and aligns proof-gated rankings with those from the full-history prompt to limit information loss.Source: §4.3 · p. 4, §4.1 · p. 4

4.4 Evidence-to-Proof: Does the Claim Come from the Selected Evidence?

The grounding check tests whether each preference claim depends more on its selected evidence than on a comparable control entry. Controls are chosen from unselected history entries, with matching rules based on polarity, position, and category.

4.4 Evidence-to-Proof: Does the Claim Come from the Selected Evidence?

  • In the audio example, the comfort claim is tested by comparing masks of the comfort review E1 and control review E3.The claim score should fall more when the selected E1 review is masked.Source: Figure 2 · p. 5
  • For each selected entry, the control is the nearest unselected entry with matching polarity; category and earlier position break ties.If no unselected entry has matching polarity, the control is chosen from all unselected entries; without ratings, recency applies with category tie-breaking.Source: §4.4 · pp. 4–5
  • The grounding contrast compares a claim’s score when its selected evidence is masked with its score when a control entry is masked.Both scores use masked histories, so the contrast isolates the selected entry’s effect relative to the control rather than to the intact history.Source: §4.4 · pp. 4–5
  • A grounding failure occurs when masking the selected entry lowers the claim score no more than masking its control.The grounding loss averages a softplus penalty over valid links; users without valid links contribute no grounding term.Source: §4.4 · p. 5

4.5 Proof-to-Recommendation: Does the Claim Affect the Recommendation?

The influence check removes a preference claim while holding selected evidence fixed, then measures the target’s ranking-margin change. Ranking preservation aligns proof-gated scores with a full-history probe, and the training objective combines recommendation, verification, and preservation terms.

4.5 Proof-to-Recommendation: Does the Claim Affect the Recommendation?

  • Deleting a claim while keeping selected evidence fixed tests what the claim text adds beyond the evidence it cites.Influence is measured as the change in target margin against the strongest sampled competitor; a positive contrast means deletion reduces the target’s lead.Source: §4.5 · p. 5
  • A small influence contrast can count as failure even for a correct claim that repeats another, encouraging Pass B to draw on each retained claim.The objective seeks a smaller target margin after claim deletion, not necessarily a different top-ranked item.Source: §4.5 · pp. 5–6

4.6 Ranking Preservation

  • Ranking preservation aligns proof-gated and full-history ranking distributions using the same comparison set, scoring function, and temperature.The full-history distribution is a stop-gradient passive probe, not a separately trained recommender; recommendation accuracy comes from losses on the proof-gated context.Source: §4.6 · p. 6

4.7 Training Objective

  • Training groups proof and item recommendation losses separately from grounding and influence losses, then combines them with ranking preservation.Training proceeds by first fitting Pass A, then Pass B, and finally jointly optimizing with masked-evidence and claim-deleted views.Source: §4.7 · p. 6

4.8 What the Theory Guarantees

The theory relates verification losses to failure rates and gives a condition for top-K preservation. The preservation guarantee applies to the sampled comparison set and depends on the full-history ranking’s boundary margin.

4.8 What the Theory Guarantees

  • Theorem 1 bounds the frequency of grounding and influence failures by the corresponding average verification losses divided by log 2.Grounding failure means the selected-entry mask hurts the claim no more than the control mask; influence failure means claim deletion leaves the target’s lead intact.Source: §4.8 · p. 6
  • When the full-history boundary margin exceeds 2√δ, the top-K set is unchanged if the preservation divergence is at most δ.This guarantee concerns the sampled comparison set; full-catalog Recall and NDCG are covered only when that set is the whole catalog.Source: §4.8 · p. 6

5 Experimental Evaluation

The evaluation tests recommendation performance, component contributions, reasoning designs, a qualitative case, and whether verification strengthens evidence dependence and decision relevance. PROVE-Rec achieves the best results across three datasets, while ablations and verification tests examine the contributions and behavior of its components.

5.1 Datasets, Preprocessing, and Preference-Proof Construction; 5.2 Experimental Setup

  • Evaluation covers Grocery, Tools, and Yelp, comparing PROVE-Rec with sequential, item-tokenization-based LLM, and LLM-enhanced recommenders.Examples retain at most 20 pre-target interactions; ratings and reviews are included for the three most recent entries, and target and future information are excluded.Source: §5.1 · p. 7, §5.2 · p. 7

5.3 RQ1: Does Verifiable Preference Reasoning Improve Recommendation Performance?

  • PROVE-Rec achieves the best result on all three datasets, improving 2.01%–7.45% over the strongest baselines per metric, with an average gain of 4.17%.The largest improvement is 7.45% on Yelp for NDCG@5.Source: §5.3 · pp. 7–8, Table 3 · p. 8

5.4 RQ2: Are Preference Claims Grounded and Influential?

  • Direct Recommendation performs worst, with a 12.0% average relative decrease; removing grounding, influence, and ranking preservation decreases performance by 6.1%, 4.3%, and 2.4%, respectively.The full model achieves the best Recall@10 and NDCG@10 on all three datasets.Source: §5.4 · p. 8, Table 4 · p. 8

5.6 RQ4: Qualitative Case Study

  • In the Grocery case, evidence entries E1, E3, and E5 support several preference claims, and PROVE-Rec ranks the held-out instant miso soup first.The authors describe this case as an inspectable reasoning path, not evidence of a general performance advantage.Source: Table 6 · p. 9, §5.6 · p. 9

5.5 RQ3: Which Information Sources and Reasoning Designs Matter?

  • Ratings improve over item-only histories, reviews yield larger gains, and combining both signals performs best among input-only variants; structured proofs outperform free-form rationales across datasets.The full model performs best after additionally verifying both reasoning links while preserving recommendation utility.Source: §5.5 · p. 9, Table 5 · p. 9

5.7.1 Evidence-to-Proof Verification.

  • PROVE-Rec reaches evidence-grounding success rates of 76.8%, 73.9%, and 67.4% on Grocery, Tools, and Yelp, respectively.Its grounding contrasts are 0.049, 0.041, and 0.025 on those datasets.Source: §5.7.1 · pp. 9–10, Table 7 · p. 10

5.7.2 Proof-to-Recommendation Verification.

  • Claim-influence success reaches 82.1%, 80.3%, and 72.6% on Grocery, Tools, and Yelp, respectively, while format sensitivity remains below the Structured Proof baseline.The test removes one claim while keeping selected evidence and remaining claims fixed, then recomputes the target ranking margin.Source: Table 8 · p. 10, §5.7.2 · p. 10

5.7.3 Empirical Check of the Theoretical Bound.

  • Empirical non-positive grounding and influence rates remain below conservative theoretical bounds; uncached latency is approximately 313 ms versus 65 ms direct, while cached Pass B takes approximately 63 ms.The bounds confirm the one-sided guarantee and are not intended to tightly estimate empirical rates; proof caching permits reuse when user history is unchanged.Source: Table 9 · p. 10, §5.7.3 · p. 10, §5.7.4 · p. 10

6 Conclusion

On three review-rich benchmark datasets, PROVE-Rec improves recommendation performance while achieving stronger evidence grounding and decision influence than review-aware and reasoning-enhanced baselines. It links compact preference claims to historical evidence, gates recommendation through the proof, and uses a preservation objective to maintain recommendation quality.

6 Conclusion

  • On three review-rich benchmark datasets, PROVE-Rec improves recommendation performance while achieving stronger evidence grounding and decision influence than review-aware and reasoning-enhanced baselines.The authors conclude that recommendation reasoning should be evaluated by whether evidence supports stated preferences and whether those preferences affect the recommendation.Source: §6 · p. 10
  • PROVE-Rec links compact preference claims to selected historical evidence and gates recommendation through the generated proof, while a preservation objective maintains recommendation quality.Source: §6 · p. 10
  • Table 10 reports average input and proof lengths, per-example latency, and peak PyTorch GPU memory on one NVIDIA RTX A6000.Source: Table 10 · p. 11
  • The authors caution that inferred preferences may expose sensitive information or encourage over-trust, and recommend privacy safeguards and clear communication that proofs are model reasoning, not definitive user intent.Source: §6 · p. 11

A Detailed Theoretical Analysis

The appendix section states that it proves Theorems 1 and 2, with notation following Section 4.

A Detailed Theoretical Analysis

A.1 Proof of Theorem 1: Two-link failure-rate control

The proof bounds failure rates for grounding and influence using a softplus penalty, then extends the argument to bound large failures more tightly.

A.1 Proof of Theorem 1: Two-link failure-rate control

  • The proof applies the softplus penalty ℓ(Δ) = log(1 + exp(−Δ/τ)) to grounding contrasts and assumes its expectation under ν_grd is at most ε_grd.Source: §A.1 · p. 11
  • The argument defines the failure event as Δ ≤ 0 and takes expectations under ν_grd to relate that event to the penalty bound.Source: §A.1 · p. 11
  • Replacing grounding quantities with influence quantities proves the influence bound; applying the same steps to Δ ≤ −η yields a tighter bound on large failures.Source: §A.1 · p. 11

A.2 Proof of Theorem 2: Top-𝐾preservation

The top-K preservation proof uses a positive full-history boundary margin to show that proof-based rankings preserve the full-history top-K set.

A.2 Proof of Theorem 2: Top-𝐾preservation

  • Both softmax distributions have full support on the finite comparison set, and 1 ≤ K < |C_u| ensures the Kth and next-ranked items exist.Source: §A.2 · p. 11
  • For any item inside the full-history top-K set and any item outside it, the full-history probability difference is at least γ_K.Source: §A.2 · p. 11
  • The proof concludes that the proof-based top-K set uniquely matches the full-history set, for full-catalog recommendation or within a sampled comparison set.Source: §A.2 · p. 11

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