More Than What Was Chosen: LLM-based Explainable Recommendation Beyond Noisy User Preferences
Authors: Chung Park
Hyeongjun Yun
Taesan Kim
Junui Hong
Dongjoon Hong
Mira Myong
Jihoon Oh
MinCheol Cho
Kijung Park
Min sung Choi
Jihwan Seok
Jaegul Choo
Venue: ICLR 2026 Poster (2026)
Keywords: LLM-based Recommendation
Rationale
Revealed Preference
Explainable Recommender
Primary area: interpretability and explainable AI
TL;DR: We propose C-APO, an LLM-based recommender that balances Revealed Preferences and Coherent Preferences—logically consistent with user history—for robust recommendations and rationales.

Abstract: Recommender systems traditionally rely on the principle of Revealed Preference (RP), which assumes that observed user behaviors faithfully reflect underlying interests.
While effective at scale, this assumption is fragile in practice, as real-world choices are often noisy and inconsistent.
Thus, even LLM-based recommendation models (LLM-Rec) equipped with advanced reasoning capabilities may fail to capture genuine user preferences and often produce rationales of limited persuasiveness.
To address this issue, we introduce the concept of Coherent Preference (CP), which complements RP by favoring items that are logically and causally coherent with user interaction history.
Building on this perspective, we propose Conflict-Aware Direct Preference Optimization (C-APO), an LLM-Rec framework that jointly optimizes RP and CP while adaptively reconciling their agreement and conflict, delivering robust recommendation performance and logically consistent rationales. 
We construct a unified ordering approach that combines the RP signal, based on chosen versus unobserved items, with the CP signal, which ranks items by their logical consistency with past interaction history.
In this unified preference ordering, we dynamically adjust the influence of each signal depending on whether RP and CP agree or conflict, allowing the model to better capture user intent and generate more plausible recommendations.
On the Amazon Review dataset, our approach consistently outperforms approximately 20 state-of-the-art baseline models in both recommendation performance and rationale quality, achieving a 1.65$\times$ relative improvement in click-through rate during deployment, thereby demonstrating its practical utility.
The code and dataset are available at https://github.com/cpark88/C-APO.

Published as a conference paper at ICLR 2026
MORE THAN WHAT WAS CHOSEN: LLM-BASED Ex- PLAINABLE RECOMMENDATION BEYOND NOISY USER PREFERENCES
Chung Park Hyeongjun Yum Taesan Kim- Junui Hong' Dongjoon Hong* Mira Myong* Jihoon Oh' MinCheol Cho' Kijung Park Min sung Choi² Jihwan Scok! Jaegul Choo
SK Telecom Korea Advanced Institute of Science & Technology (cpark88kr, rpaghdltm, ktmountain1599, [dongjoon. hong, mira.myong, myong, skt. jihoon, skt. mccho (skk com, wejuung.ark. ms cchc, seokjhJask. com, jchoo@kaist ac. kr
secendrun3 e i com,
ABSTRACT
Recommender systems traditionally rely on principle of Revealed Preference (RP), which assumes that observed user behaviors faithfully reflect underlying in- terests. While effective at scale, this assumption is fragile in practice, as real-world choices often noisy and inconsistent. Thus, even LLM-based recommendation models (LLM-Recc equipped with advanced reasoning capabilities may fail to capture genuine preferences and often produce rationales of limited persua- siveness. To address this issue, we introduce the concept of Coherent Preference (CP), which complements RP by favoring items that are logically and causally coherent with user interaction history. Building on this perspective, we propose Conflict-Aware Direct Preference Optimization (C-APO), an LLM-Rec frame- work that jointly optimizes RP and CP while adaptively reconciling their agree- ment and conflict, delivering robust recommendation performance and logically consistent rationales. We construct a unified ordering approach that combines the RP signal, based on chosen versus unobserved items, with the CP signal, which ranks items by their logical consistency with past interaction history. In this unified preference ordering, we dynamically adjust the influence of each sig- nal depending whether RP and CP agree conflict, allowing the model better capture user intent and generate more plausible recommendations. On the Amazon Review dataset, our approach consistently outperforms approximately 20 state-of-the-art baseline models in both recommendation performance and ratio- nale quality, achieving 1.65 relative improvement click thek-throughh rate during deployment, thereby demonstrating its practical utility. The code and dataset are available ht/httts///ggthh// com/ caark88/ Bwtrkka//--- C-PPO
INTRODUCTION
Recommender systems fundamentally aim to model user preferences. Traditional approaches are based on the concept of Revealed Preference (RP) (Samuelson) 1938) from microeconomics, which assumes that observed user choices reliably reflect underlying preferences. Conventional recom- mender systems, such as collaborative filtering (CF) models, instantiate this view by learning from user-item interaction histories and have achieved remarkable performance in research contexts.
However, real-world choices are often noisy, inconsistent, or shaped by transient emotions, contex- tual constraints, and limited information. Consequently. the items users select do not always align with their stable or meaningful interests (Ahn & Lin (024). In our analysis of the Amazon Re- view dataset (Fig. [], using LLM--as-a-udge (Gu et al.) (024) to assess logical coherence with prior behavior, roughly 30% of the chosen (ground-truth) items could not be logically explained, suggesting that RP alone may be insufficient to capture true intent.
'Contact: spark&8kr@ggaill.com wit.cparr@sk.ccmm

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Published as a conference paper at ICLR 2026
Matoricul
Fantasy
Romance
Fantasy
Romance
(a) Clothing
(b) Health
Internction
Ratio(%)
Coherent Proference (CP)
Revealed Preference (RP)
Romance Genre
Action Genre
Unoosereed
but Coherant liam
Figure 1: Distribution of LLM coherence scores (1-7) for chosen items based on user history, with a substantial portion falling below 4, indi- cating frequent divergence from past behavior.
Figure The chosen item (Action) diverges from the user's is romantic-history pattern, whereas the unobserved item (Romance) better aligns with preferences inferred from viewing history.
Consider a user who consistently watches movies in the Fantasy and Romance genres (Fig. 2) At some point, the user watches an action movic-an atypical choice given their prior viewing history. Such a choice may not reflect the true preference of users, as it may be driven by shared account usage, social viewing contexts, or transient factors such as promotional events. A romantic fan- tasy film- aligned in emotional tone and narrative structure with the user's history- would likely serve more coherent and persuasive recommendation. This motivates Coherent Preference (CP): the preference over items that are causally aligned with prior behavior. Inspired by behavioral economics, CP complements RP by asking not only what was chosen, but what would likely be chosen if behavior were consistent and explainable. This perspective highlights a core limitation of the training paradigm in current LLM- -baeed recommenders (LLM-Rec), as methods such as Di- rect Preference Optimization (DPO) (Rafallv et al.) (023) typically rely on the RP ordering (chosen unobserved). Consequently, despite their advanced reasoning capabilities, LLM- models may not fully capture genuine user preferences and thus often produce rationales that lack persuasive- ness (Tsai et al.] 2024). This limitation is particularly critical in platforms such as Instagram or Amazon, where exposing both recommendations and their rationales may undermine user trust.
To address this issue, we introduce Conflict-Aware Direct Preference Optimization (C-APO), an LLM-based recommendation framework that jointly models RP and CP orderings while adaptively reconciling their agreement and conflict. While RP captures observable user-item interactions that boost recommendation performance, CP complements it by modeling the reasoning behind besbcee--bnneficial for inferring intent and generating persuasive rationales. For each user, we con- struct triplet consisting of the ground-truth chosen item and two unobserved alternatives-items the user did not interact with, which we hereafter refer to as rejected items. A state-of-the-art LLM is then prompted generate, for each item, natural-language rationale explaining its relevance to user interaction history along with a coherence score. Of the two rejected items, the one with the higher CP coherence score is labeled hard rejected, and the one with the lower score easy rejected. Each triplet element includes both the item and its post-hoc rationale, with the coherence scores further validated through human evaluation and statistical testing.
In C-APO, we first establish the RP ordering, which always ranks the chosen item above all rejected ones (i.e., chosen > hard easy rejected). On top of this, CP introduces a secondary ordering among the rejected items based on coherence scores (i.e., hard > easy), yielding the unified triplet ranking chosen > hard rejected > easy rejected. However, CP is not limited to ordering among the rejected items: it can also compare the chosen item against the rejected ones. This is where the interaction between RP and CP emerges-
occurs when the chosen item also receives the highest coherence score, while conflict arises when a rejected item is judged more coherent, indicating that the chosen item may not fully reflect genuine user preference. Therefore, while DPO relies solely on pairwise comparisons between the chosen and rejected items based on RP, C-APO introduces a unified ordering that integrates both RP and CP over all items and adjusts their alignment and conflict. With our conflict-aware adaptive weight, it probabilistically reconciles the two signals by strengthening CP when aligned with RP, and diminishing it when conflicting. This helps the model avoid overfitting to noisy signals in RP by leveraging the reasoning- perspective of CP.
We also provide gradient analysis of how C-APO modulates the learning dynamics. When the chosen item shows low coherence with user interaction history- -ie., when the RP and CP orderings are in conflict- its likelihood is effectively suppressed, reducing its selection probability.
We conducted large- scale experiments across five Amazon Review domains, comparing our method against approximately 20 state-of-the-art baselines. Our method consistently outperformed most
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baselines in both recommendation performance and rationale quality. Furthermore, in real-worll online A/B test, our model achieved a 1.65 x relative improvement in click- thrikkhhroughhh rate over existing models, demonstrating its practical effectiveness. We highlight three key contributions:
We introduce the Coherent Preference and present a data construction recipe for generating ratio- nales with coherence scores, releasing the entire dataset publicly despite the high API/GPU cost.
We develop C-APO, a reinforcement learning method that adaptively and probabilistically recon- ciles conflicts between RP and CP orderings, improving both generalization and rationale quality.
Our method outperforms about 20 baselines across five Amazon Review domains and achieves significant gains in online A/B testing, demonstrating real-world applicability.
2
MOTIVATION AND PRELIMINARIES
2.1 PREFERENCE AND RECOMMENDER SYSTEM
Traditional recommenders are built upon Revealed Preference (RP), which infers user interests directly from observed interactions such as past purchases or clicks. Yet, real-world choices are often noisy and shaped by transient factors, making RP an imperfect proxy for stable intent (Ahn & Lin 2024). To complement this, we introduce Coherent Preference (CP), which focuses on items that are logically consistent with prior behaviors what users could should prefer under coherent reasoning. CP represents a behavioral economics-inspired critique and extension of the classical RP paradigm (Hédoin 2016), while introducing interpretability and generalizability into LLM-Rec. While the RP signal provides a user-item interaction signal, the CP signal complements it by capturing the reasoning behind choices-an an aspect that is particularly useful when modeling user intent or generating explanation rationales.
2.2 PROBLEM SETUP AND TASK DEFINITION
Definition 1: User Behavior History. We assume sequential recommendation task setting where a user u E 2/ interacts with a sequence of items S, [ ig, ...,...,.. ir]. where each i, E I is an item from the whole item set Each item it represented by a tuple containing the item's 'ss title and description. Then, we set the chosen item i, E I that the user interacted with next. Based on this formulation, we define the user-item interaction dataset D {(S,, ie) u U). Note that we denote the ground-truth item the chosen item and non-interacted items rejected items.
Triplet Rationale Dataset Construction Recipe. The chosen item ic is the user's next observed choice, reflecting their revealed preference (RP). However, RP does not always exhibit logical coherence with prior behavioral patterns. Accordingly, we constructed a dataset to model coherent preference (CP). We randomly sampled two unobserved items ficms-ie., items the user has not interacted with-i, and iz from the whole item set such that i7 in & S. U (iz). For each item i (i., 'T (5 ). we prompted a state-of-the-art LLM to generate: (1) a natural-language rationale r explaining why item i might be recommended given S,, and (2) coherence score E {1, 21.2.....7 7) assessing the logical consistency and persuasiveness of the corresponding rationale. This score 7: Very Strong (highly coherent
Definition 2:
follows a rubric ranging from 1: Very Weak (no connection) and contextually perfect). In the I.L.-as-a-Judee framework, this approach is referred to as single- answer grading (Gu et al. 2024). We validated the LLM-based coherence scores against human expert and annotator ratings, confirming substantial agreement with human judgments (Spearman's p = 0.71, p 0.0001). All validation procedures are provided in Appendix D
We then compared the coherence scores 8 of i and iz to determine the CP ordering- The item with the higher score was labeled as the hard rejected item in. and the one with the lower score was labeled as the easy rejected item i.. Each constructed instance is represented as a unified ordered triplet (ie > in > ic)- ieat is, RP implies ia > in and is > le. while CP implies in > ie- CP can also induce an ordering between the chosen and rejected items based on coherence scores, which is not included in the fully ordered triplet (i. > in > is) but is used to assess agreement or conflict with RP and modulate the unified ordered triplet accordingly. For each item i, we attached its generated rationale r and the corresponding coherence score s, and wrote y = (i, r, s); thus the instance can be expressed as the totally ordered triplet (ye > yh > Ve), with ye (ec, Fe, 8c), Yh, (in, Thy st). and Ye. = (ie, ras s.) --indicating that our model not only learns the recommended item, as in prior work, but also the rationale supporting its recommendation. Empirically, shown in Fig.
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found that although ie often received higher coherence scores than the rejected items, there is nontrivial fraction of cases in which ie scores lower, highlighting a conflict between RP and CP.
Although extending beyond two rejected items is possible, it would substantially increase the cost of generating rationales and scores with the LLM as well as the training overhead (Cai et al. 2025). Since our model captures RP-CP alignment effectively with two rejected items, we leave such tensions to future work. We have released our dataset despite substantial LLM API and GPU costs
Problem Formulation. For given user with interaction history sequence S.
[1, 12.-.... ir), the model aims output tuple (i, where i denotes the next recommended item and A is natural-language explanation justifying the recommendation.
Chesen < Reeeectdd (%)
50
Amazon Fashion
Grocery and Gournet Food
Industrial and Scientific
Clothing Shoes and jeweryy
Health and Household
40
40.3%
34.5%
33.8%
91
35. 7%
16
20
10
6.4%
5.2%
6.3%
5,7%
0.8%
Hard
Easy
Hand
Easy
Hard
Easy Hard
Easy
Hard
Easy
Figure 3: A non-trivial portion of cases across five domains of the Amazon Review exhibit higher coherence scores for rejected items compared to the chosen item, indicating conflicts between Re- vealed Preference (RP) and Coherent Preference (CP).
CONFLICT-AWARE DIRECT PREFERENCE OPTIMIZATION (C-APO)
3.1 RECALL OF DIRECT PREFERENCE OPTIMIZATION (DPO)
Supervised Fine-Tuning (SFT). In general, most alignment approaches (i.e., reinforcement learn- ing) in recommendation, including Direct Preference Optimization (DPO) (Rafailov et al. 2023). are performed following supervised fine-tuning (SFT) stage on recommendation-speenn speciifc data. this stage, the LLM backbone is trained with a causal language modeling loss prompts containing user history and completions consisting of chosen items and rationales, and all subsequent descriptions are based on the tuned LLM obtained after SFT. Further details of SFT are provided in Appendix C
Direct Preference Optimization (DPO). DPO does not rely on explicitly training reward model; instead, it infers the optimal policy directly from pairwise preference data, namely chosen versus rejected items. To achieve this, the reward function g is reparameterized in terms of the optimal policy through a formulation:
#e(ylz) where me is the trainable target LLM and g(r, y) 3 log <
Tet is the reference model, with the partition function omitted for simplicity. For the preference learning dataset D {(I., Me: y }}. where F, is input prompt constructed from user history S. (and related context), and Ye and y denote the chosen and rejected items, respectively, preference can be formulated by the Bradley-Terry model as p(ye y x.)) a(( Ve) g( y ))- DPO formulates the preference likelihood as the following objective:
Te(y (xT) logo B log To(Nokzu) 3 log Teat (ye (..)
(oro(To; weet) -E (uu. Be.y JND
(1)
Teat Trorly y (Ta)
However, existing applications of DPO in recommender systems primarily rely on RP, under the assumption that the chosen item is preferred over the rejected one. To overcome this limitation, we propose C-APO, novel framework that integrates both RP, which captures user-item interaction signals, and CP, which models the reasoning behind choices- improving recommendation performance and enhancing the persuasiveness of generated rationales.
3.2 DERIVATION OF C-APO
As mentioned in Section 3.1 we given dataset of user preference triplets D {(I, yc: Mh. y. To align L.M--Rec with triplet preference, we first derive the preference distribution.
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Plackett-Luce (PL) model For prompt 2u and three candidate responses (yer Uh Vel, the Plackett-Luce (PL) probability of permutation T is as follows:
3
e (90(wn.).
p(T Mes Uhe Ve: F.)
II
(2)
Si= (ou(e..)rno))
where ex denotes the exponential function, with the shorthand used in place of exp(-) due space constraints, and the implicit reward given by go(z, y) = 3 log )w(()))) An example of the prompt used for this task is provided Appendix E. which is also used in our C-APO framework.
ra(glz)
For the desired full ranking Ye Yh > Ves let T (ye, yh, De) Within r*, RP is formalized as Ve > Yh and Ye > ye- whereas CP is formalized as yi > Ye- With shorthand = Do (T ye), On. (F Wh), Ie = go (Tur Ve), Eg. 2 can be rewritten as:
(uv > yn Ue kw) (7" Mes Uh. Me: (u) S6 e Sh
(3)
9h + 9c
We maximize the log- liielihhodd of the complete ordering; the loss function is therefore the negative log-probability:
LPL. Ba
Ve Be. D log p' : > yh > Ve xn)]
(4)
Substituting Eq. 3 into Eq. 4 and simplifying yields:
CpL. = -B log or
(-log log (
(0n (s)) + (oc (0)))))) + log -log (e ((laaTo))))
(5)
(1) Revesled Preference (RP)
(2) Coherrntt Preference (CP)
Eq. shows that the PL model jointly enforces the full ordering in single objective, thereby pre- serving permutation consistency. The first term in Eq. corresponding to RP, encourages the model rank the chosen Ye. higher than the rejected items y/. and Ye- The second term, based the coherence score, explicitly models the CP ordering between and ye- However, the PI. model does not directly model the ordering between chosen and rejected items from the perspective of CP. As a result, the first term in Eq which reflects RP. cannot correct for potential noise inherent in user interactions. Notably, the CP signal can also capture the relative ordering between chosen and rejected items via LLM-ggnerated coherence scores. This introduces potential source of agreement or conflict between RP and CP signals. To this end, we propose conflict-aware weighting mechanism that adjusts the contribution of the RP and CP signals in Eq. 5
Conffict-Aware Adaptive Weight previously discussed, while ye always preferred over all rejected items from the RP perspective, this may not hold from the perspective of CP. In cases where the coherence score S. of the chosen item is lower than that of 8 or s indicctinn a conflict be- tween RP and CP-wc down-weight the relative reward of the chosen item compared to the rejected ones using a trainable weight. Conversely, when RP and CP are in agreement, the chosen item's relative reward is further emphasized. As result, the model avoids overfitting to behavior-only signals and instead learns to generate more coherent rationales, effectively mitigating noise in the RP signal.
We define the conflict-aware reward difference as Wi - gj). where way denotes the trainable conflict-aware adaptive weight assigned to each item pair i,j hc, This weight reflects the probabilistic advantage of item over item j based on their coherence scores. Then, we replace the term (gi 9ÿ) in Eq. with W5.5 (g. - 9;), and rewrite this equation as follows:
LCAAPO - -R de M..
log log manlan se) Me.el0e ac)
log o7
log Mh. vn))
(6)
CYConiiir-Aooe Revealed Perfenmer aliguot Coburrezt Prefermne
The detailed formulation of the conflict-aware adaptive weight waj is as follows. Although these co- herence scores show a statistically significant positive correlation with human ratings (Section (2.2). treat them as noisy observations rather than gold-standard references, even the state-of- offthhe-mm art (SOTA) LLM may produce inconsistent evaluations. To calibrate them, we re-examine the same inputs used for coherence scoring user history S., item and post-hoc rationale r-throughh
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smaller text encoder (e.g., SBERT), which provides an additional stage of validation for the scoring performed by the SOTA LLM.
Specifically, we use the LLM coherence (S. i, r) E RBxD where fie is a frozen text encoder, and &u denotes its output representation for batch of size B and hidden dimension D. Inspired by the Thurstone-Mostellee model (Thur-
along with the text encoder feature 2u
stone 2017 Mosteller 1951); see Appendix | for details, we compute the mean and standard
deviation a of the score calibrate it as follows:
== + Gate FCI(zu), न = so
where Gate is sigmoid activation followed by fully connected layer, and FC1 and FC2 are trainable fully connected layers with ReLU activation. All outputs-Gate(zu)) (z.), and FC2(Zu) are in RBx1 We convert these into soft pairwise weight:
We.h Φ Pyg
PRR ". We.e /6 Muc
- Mye be
o Mon Mue.
Whee
Von + 0
where is the Gaussian cumulative distribution function. mapping the weight to the [0. range. The resulting waj modulates C-APO updates, amplifying RP-CP agreements (large Wi,j) and atlen- uating uncertain conflicts (small wa.r). By softly adjusting the loss in proportion to this preference inconsistency, the model guided favor recommendations where RP and CP are aligned. Note that our objective Eq. strictly generalizes PL/DPO; see Appendix K
Gradient Analysis We conducted gradient analysis on C-APO. Let Agi (gi - gi). and denote V (rg yk) for brevity; then, the gradient of LC-APO with respect to parameters & takes the following formulation, where 81 log (e (eWe,AAsh.. + e (Be.e and 82 = Whee Age.k-
VoLCAAPO - -.E (s)) Mone Meche Aonie (V.-va) Wose Ae.e (V. (V.-Ve) a(s2) Who (vA- Vo)
"o Auh.eeseeee MeleApe.e
(8) The gradient of the C-APO loss in 8q. mainly governed by two components. First, the gradient for the chosen item points in the direction that increases the likelihood of yes while the gradients for the rejected items push the model to decrease the likelihoods of yh and ye, respectively. The strength of this effect is scaled by waeh and We,e. When RP and CP are aligned-indieenn We.h or the gradient more aggressively increases the likelihood of the chosen item, while applying weaker updates the rejected items. Second, the ax-) terms act modulation factors that reflect the degree of ordering inconsistency. Specifically, a(s)) increases when the reward of yı or ye exceeds that of ye. i.e., when Agh,e > 0 or Age,e >> and (s2) increases when the reward of We surpasses that of yn. (i.e., Age,h > 0). This results in larger gradient magnitudes applied to push up ye, encouraging the model correct the error by increasing the likelihood of the chosen item. See Appendix for gradient derivation details.
by large
4 EXPERIMENTS
Our experiments aim to answer the following questions: (RQ1): Does our model achieve superior recommendation performance over CF-Rec and I.M-Rec baselines? (RQ2): Are the recommen- dation rationales generated by our model more persuasive than those produced by other LLMMRec baselines? (RQ3): Does incorporating coherent preferences alongside revealed preferences lead to improved recommendation performance? (RQ4): Does our conflict-aware adaptive weight module lead to improvements in recommendation performance? (RQS): the performance of the model ro- bust with respect to variations in the hyperparameter? (RQ6): Does our model demonstrate superior performance to existing baselines in online A/B testing?
4.1 DATASETS
Table 1: Statistics of Datasets.
We conducted extensive experiments across five diverse domains of Amazon Review 2023 (Hou)
Dataset
#Users #Items
Avg Item Length Purchase Sparsity
Avg
et al. 2024), comparing our model against nearly twenty baselines to demonstrate its broad applica- bility (Table []. The dataset consists of domains with practical relevance relevanc to real-world applications, including Amazon Fashion, Grocery and Gourmet
Fashion
11,028
59,004
6.54
1.23
0.9994
Grocery
11,334
6.149
10.76
21.02
0.9926
Scientific
18,275
7.053
6.69
17.43
0.9956
Clothing 13,766
107.353
11.83
1.65
0.9994
Health 21.152 74,784
8.49
2.50
0.9994
Food, Industrial and Scientific. Clothing Shoes and Jewelry, and Health and Household.

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4.2 EXPERIMENTAL SETUP
Baselines and Evaluation Settings We assessed the performance of our model by comparing it to two categories of baselines: (1) CF-based (CF-Rec) and (2) LLM-based Recommendation model (LLM--Rec) as shown in Table 2 We adopted Gemma-3-44 4B-it (Taa et al. (025) the LLM backbone, taking into account deployment constraints in real-world environments. However, the scaling-law trend (c.g., IB, 4B, 12B) is observed in larger models (Appendix M). We evaluated rec- ommendation performance using the leave-one-our method. For each user sequence, the last item was used for testing, the second-to-lad Io-st for validation, and the rest for training. We adopted com- mon approach by pairing the chosen item (positive) with rejected (negative) items the user has not interacted with. These items form the candidate set and incorporated into the prompt construc- tion for training and inference. We focused on standard evaluation metrics, including Hit Ratio (HR) and Normalized Discounted Cumulative Gain (NDCG). All experiments were conducted using two NVIDIA H100 GPUs. For reproducibility, implementation details are described in Appendix A
Table 2: Model performance comparison on Amazon, with the top two methods highlighted in bold and underline, respectively.
Domain
Fashion
Grocery
Scientifie
Clothing
Health
Types
Models
|RR@1
HR
N
HR
HR@5
N@5
|R@ 1 HR@5
N@5
[RR@1
HR
No
HR
HR@s N
SASRec
3.47
17.71
10.39
4.22
18.39
11.20
3.15
16.15
9.50
3.37
16.74
9.89
2.67
13.88
8.15
BERT4Rec
1.62
6.90
4.19
4.27
18.97
13.41
7.80
2.19
12.14
7.02
2.45
7.53
S3Rce
3,38
14.98
9.09
2.92
14.27
8.46
4.17
17.88
10.91
3.17
12.83
7.91
2.51
12:45
7.37
NextlrNet
1.93
6.31
4.15
2.89
15.00
4.88
24.64
14.52
3.35
13.75
8.45
2.70
13.36
7.94
CF
GRU4Rec
SINE
2.43
1.63
15.34
8.38
8.69
4.85
3.82
3.08
15.11
17.13
10.28
8.96
2.58
3.13
13.72
15.76
9.30
8.01
4.71
3.12
13.33
19.14
11.89
8.12
2.92
3.88
14.19
17.84
8.43
Rec
TransRec
2.27
12.14
6.92
4.79
18.70
11.62
5.33
24.43
14.71
3.36
13.95
8.56
2.80
13.61
8.10
ANs
2.84
13:97
8.25
4.42
18.10
11.13
3.11
15.62
9.21
2.95
15.85
9.25
2.80
13.71
8.13
STAMP
2.84
23,51
12.76
3.69
16.80
10.11
3,46
15.78
9.48
3.45
13.89
8.57
2.72
13.61
8.03
FPMC
1.55
5.27
3.33
4.55
18:27
11.36
4.43
20.10
12.18
2.62
10.59
6.55
2.95
13.39
Rec SAVER
4.27
24.41
14.34
5.73
18.04
12.19
6.10
17.41
12.05
5.66
22.03
13.89
2.86
14:65
8.61
SumRecomm
8.06
28.65
18.55
3.84
15.19
9.89
4.62
14:43
9,90
3.97
18.74
11.82
3,27
15.10
9.31
RecLM
5,86
9.77
6.62
5.52
10:60
8.56
2.59
7.13
5.96
2.25
7,08
5.51
1,90
6.01
5.13
LLM
S-DPO
5.22
23.78
14.38
6.57
18.47
12.91
7.57
20.60
14.84
4.16
19.37
11.75
2.76
15.71
9.28
Roc
GRAM
5.88
17.12
11.54
1.75
5.22
3.52
4.26
11.85
8.11
3.34
11.76
7.54
4.55
13.39
9.00
Intuitor
4.46
10.46
7.71
6.34
11.58
8.92
7.10
16.39
12,57
3.22
9.53
6.43
3.30
8.62
6.19
Rec-RI
8.43
29.29
18.80
5.65
17.95
11.94
4.82
20.67
13.44
5.62
21.88
14.06
2.33
14.24
8.03
SLMRec
3,21
6.58
4.87
3,44
11.11
7.34
3.27
24.66
13.75
3.19
17.71
10.12
2.77
12.67
7.63
LLMEmb
3,22
7.71
5.15
3.59
14.61
8.18
4.11
13.88
8.57
3.41
16.80
9.97
2.48
7.59
6.39
Ours
9.47
28.22
18.87
6.90
19.47
13.62
12.22
22.74
17.97
7.11
23.12
15.09
4.83
18.21
11.73
4.3 PERFORMANCE EVALUATION (RQ1)
In Table 2 we reported the performance of all the baselines and our model. Our model consis- tently outperformed state-of-the-art CF-Rec and LLM-Rec models across all domains, demon- strating superior overall recommendation performance. Specifically, our model achieved rel- ative HR @1 gains over the second-best model in each domain, with gains of +12.34% (Fash- ion), +5.02% % (Grocery), +61.43% (Scientific), +25.62% (Clothing), and +24.48% (Health). In addition to HR@I I, our model consistently demonstrated superior performance across most do- mains on HR@5 and NDCG@5. This suggests that the enhanced generalization capability of the model is attributable to the integration of CP alongside RP. Even when replacing the LLM back- (Team, 2025) for both our model and S-DPO the second-
bone with -eeeen---b--nnstture best Careiine---AOO achieved 15.38 % and 12.29 % higher @5 and NDCG@S, 5, respectively. demonstrating that the performance improvement originates from our training approach rather than the choice of the LLM backbone.
4.4 EVALUATING RATIONALE QUALITY (RQ2)
We conducted a quantitative evalua- tion of the rationales produced by our model, applying an identical prompt across five baselines: Gemma- 3-4B--4- -4Biit. Genmaa-3 3 1----2--111 it, Rec-SAVER, DPO, and S-DPO. From each of the five do- mains, 300 samples were randomly cho- sen, yielding 1,500 evaluation cases in to- tal. Our assessment followed two-stage
Keb Cencer Mndk saivoll
k Carrert & eetuadeesiiiina
Fastion
Food
broton)
1-28
(9)
S-O0/AR1
Figure 4: LLM-as-a-Judge of Rationale Quality
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protocol: first checked whether the recommended item belonged to the valid item vocabulary, and subsequently examined the plausibility of the accompanying rationale. Following the evaluation protocol of Wang al. (2022). ChatGPT was used as evaluator with four- scale: 0 = Incor- rect (Hallucination), = Correct but Weak Rationale, 2 Correct with Reasonable Rationale, and 3 Correct with Persuasive Rationale. Importantly, the evaluator L.M (e.g., gpt-44- ris) differs from the LLM used to generate rationales in the training data, preventing potential evalu- ation bias. To assess reliability, we compared LLM-based judgments with human annotations on 250 sampled examples and found statistically significant agreement (Quadratic Weighted Kappa QWK = 0.75, p < 0.0001). As illustrated in Fig(4 our approach consistently generated rationales that not only aligned with the correct recommendation but also enhanced its persuasiveness. Our model achieved the highest share of top-rated rationales (score 3), reaching S.eeSs-eeerssnnitigg an improvement of approximately 5.99%p over the second-best method, Rec-SAVER (78.34% - 84.33%; Fig 4p. Details of the ALM--s-a-Judge prompt for rationale evaluation, the human eval- uation protocol with statistical correlation analysis, and representative rationale examples are all provided in Appendix
(a) Amazon Fashion
(b) Gracery
(c) Scientific
(d) Clothing
(c) Health
Performance
15
15
15
15
15
10-
IP91
NDCGES
HIR@1
NDCGOS5
NDEG
1
ND0GgS
NDCOSS
(Al
[B)+SFT
ICH+SFT+OPO
(D)+SF++PL
AE Callbration)
(F+5STTCCAAPOOUrr)
Figure 5: Comparisons between model variants across five domains
4.5 ABLATION STUDY
Component Analysis In Fig. 5 we evaluated the effectiveness of our components through five pri-
mary model variants: (A) Base: the Gemma-3-4B-is model; (B) +SFT: the base model with
supervised fine-tuning on the recommendation dataset; (C) +SFT +DPO: the SFT model further aligned with DPO; (D) +SFT +PL (DPOPL): the SFT model further optimized with a Plack- ett Luce objective enforeing ye > yh Ye: (E) +SFT +C-APO w/o SBERT calibration: C-APO without SBERT-basee confiitt-wwar weighting; (F) +SFT +C-APO: our proposed model.
(1) Recommeedattion-Speiiie Training Needed: The base model (A) underperformed most vari- ants across domains, underscoring the need for occommendation-specif training.
The model (B), which is supervised fine-tuned the recommendation-speccff dataset using only chosen items, exhibited lower performance in most do- mains than models further aligned with preference- basedd objectives (C-F). This result demonstrates that preference-basee reinforcement learning provides additional benefits in recommendation tasks.
(2) Preference- Based Alignment Matters:
(3) Coherent Preference Improves Performance (RQ3): Comparing the variant (C) with (F), we observe that jointly modeling CP yields better performance than relying solely on RP. This result empirically indicates that complementing RP with CP promotes improved generalization in recommendation models, demonstrating that RP alone cannot fully capture human preferences and highlighting the necessity of CP.
(4) Conflict-Aware Adaptive Weight Improves Performance (RQ4): The variant model (D) cor- responds to the objective Eq. 5 namely the Plackett-Luue (PL) model, which captures the full ordering of chosen and rejected items from both RP and CP perspectives. By comparing this variant with our model (F), we isolated the effect of joint RP-CP modeling versus additionally calibrat- ing their conflicts and agreements-thh
is, the contribution of our Conflict-Aware Adaptive Weight module. As shown in Fig. 5 model (D) generally underperformed our approach, underscoring the effectiveness of our adaptive calibration in handling RP CP alignment and conflict.
To further separate the effect of the SBERT-based calibration module from the conflict- awarre adap- tive weight itself, also consider the variant (E) that removes the calibration step and relies solely on raw LLM coherence scores (pairwise difference followed by sigmoid) when forming the weights wi.j- This variant assesses how well M.---vvve soft weights perform without any text-encoder-
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based adjustment. As shown in Fig. [ this model performs better than the variant (D) but worse than the variant (F), indicating that: LLM coherence signals provide meaningful preference cues on their own, while (ii) the SBERT-based calibration and conflict-aware adaptive weighting jointly offer the major performance gains, especially under noisy or misaligned CP signals.
Study on Value of / The deviation of the LLM from the base reference policy is controlled by hyperparameter / in C-APO. We selected the value of 3 from (0.1, 0.5, 1, 1,2, 5] to explore the effect of (3 on C-APO, As indicated in Fig. 6 while model performance was generally robust with respect to 3. increasing /3 improved performance up to I, beyond which performance degraded.
Scaling Law We observed a consistent scaling-law trend (e.g., IB, 4B, 12B). All three evaluation metrics-Hie @1 I, HR@) @5, and NDCG@S 5 -mmpoveed monotonically as the number of model param- eters increased, reflecting a clear scaling effect in recommendation performance (Appendix (M).
Study on Reward of Chosen, Hard Rejected, and Easy Rejected We visualize the relationship between the coherent score gap As; Se Si, E {h. e} and the conflict-aware adaptive weight w using binned distributions (Fig. 7 Negative gaps (As I 0) indicate conflicting pairs, while large positive gaps (As >> 0) indicate aligned pairs. As As increases, w increases monotonically, indicating that C-APO strengthens the relative reward of the chosen item over the rejected item when CP aligns with RP, and weakens it when they conflict. Importantly, some variability remains within each As bin, since w is not a direct mapping of LLM coherence scores but is calibrated through an additional text encoder. This indicates that subtle discrepancies exist between the raw coherence scores produced by the LLM and the adjusted values derived through the auxiliary text encoder.
(a) Amazon Fashes
Grocury and Gountet food
Birned Distisution
Distticttion
0.50
0.51
0.50
2-- (erea))
Ac-A, Jeroa))
Figure Study of on Amazon Dataset
Figure 7: Relationship between Coherence Score
Gap (As = 8c s;) and Calibrated Weight w
4.6 ONLINE A/B TEST (RQ6)
Table 3: Online A/B Test Results
We deployed our model in a real- world production environment and conducted an online A/B test as a pi- lot study over August and November 2025. In this setting, the top-1 rec- ommendation was presented to each customer, accompanied by rationale explaining the recommendation. We employed a customized version of our model tailored to business requirements. For comparison, used three baselines: a random control, a traditional machine learning (ML) model, and an SFT-based model that also generates both recommendations and rationales. Users were randomized via stable user-level hash into four buckets with a 4:3:1:2 traffic split (Random, MI., SFT, Ours). Note that the SFT-based model had been deployed in the online test for only about two weeks at the time of evaluation; thus, although its bucket allocation was 10%, its total impressions appear lower than other buckets. As shown in Table 3 our model achieved a +60.88% CTR lift over the ML baseline. A one-sided two- proportion t-test confirms that this improvement is highly significant (Z = 39. -9, < 0.001), with 95% confidence interval (CI) of [5.40%, 5.97%] ] for the absolute CTR difference. This demon- strates robust performance gain while maintaining low inference latency, confirming the model's suitability for weal-world deployment.
Model
Impression
f Clicks|
CTR
CVR
Latency
Random
180.08K
17.90K
9.90%
1.58%
94ms/call
ML model
127.83K
11.94K
9.34%
2.01%
120ms/call
SFT
6.50K
0.88K
13.56%
1.81%
132ms/call
Ours
79.50K
11.95K
15.03%
2.60%
138ms/call
To control for the possibility that performance gains might arise merely from the novelty of dis- playing rationales, we introduced an SFT-based model that also generates both recommendations and rationales but does not use conflict-aware weighting. Since the SFT model likewise presents rationales to users, it serves as a control for rationale exposure, enabling a clean isolation of im- provements attributable to our method. Even under this stricter comparison, statistically significant CTR improvement of +1.471.47 +1.47%p over the SFT model (15.03% vs. 13.56%; Z = =3.20, p < 0.001, 95% CI = =0055% 2.37%p)) We also evaluated the conversion rate (CVR)- the
method achieved
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ratio of purchases to impressions and model achieved statistically significant improvement over all baselines.
4.7 TIME COMPLEXITY
Theoretical Analysis Let CLaM be the computational complexity the backbone LLM, B the batch size, and M = 1 + R the number of responses (one chosen, It rejected; /t - 2). During training, C-APO computes log-prold probabilities for all M responses by performing pairwise comparisons under full ordering, leading
complexity of O M CLIx). Note that the training complexity of DPO is (2 -Lax). The calibrator relies lightweight text encoder (109M parameters), and its computation is negligible compared to the LLM backbone, thus omitted from complexity analysis.
Empirical Analysis Compared to GRPO-based Rec-RI, our model trains 3.14x faster, though it 1.11x and 1.26x slower than DPO and S-DPO, respectively. This slight slowdown is expected since DPO essentially leverages only pairwise data, whereas method adopts triplet structure; however, the gap is negligible and justified by the performance improvements. When comparing the PL model to ours, the difference is minimal (1.001 x), indicating that the additional calibration step contributes almost no overhead relative to the intrinsic time complexity of the LLM (Appendix L.
5 RELATED WORK
Collaborative Filtering-based Recommendation
Collaborative filtering (CF)-based approaches have long formed the foundation of recommenda- tion systems, achieving reliable performance across various practical domains. SASRec (Kang & McAuley 2018) employs self-attention mechanisms to model sequential user-item interactions. Re- current models such as GRU4Rec (Hidasi al. 2015) and STAMP (Liu et al.] 2018) are designed to capture both short- and long-term behavioral patterns. In addition, convolutional models like Nex- UtNet (Yuan al., 2019) and modality transfer frameworks such as TransRec (He al., 2017) offer alternative approaches for encoding user-item dynamics. Despite their effectiveness in capturing historical interactions, these methods provide limited explanatory capability.
Large Language Model-based Recommendation
Recent work has investigated the application of large language models (LLMs) in recommenda- tion tasks to enhance contextual understanding and explanation generation. For instance, Rec- SAVER (Tsai et al., 2024) conditions on user context to elicit rationales for recommendations and jointly optimizes the explanation and ranking objectives. RDRec (Wang et al.) (222) introduces two-step procedure: generating rationales from user history and reviews, followed by training on sequential recommendation. Rec-RI (Lin et al.) 2025) applies Group Relative Policy Optimization (GRPO). assigning feedback based on alignment between predicted and target items. S-DPO (Chen) let al. 2024) builds DPO with softmax- based formulation, supporting multiple negative samples and improving preference ranking. See Appendix B for LLM-Rec baseline details.
6 CONCLUSION AND FUTURE WORK
We presented C-APO, a principled framework that jointly models revealed and coherent preferences. This approach effectively reconciles agreements and conflicts between the two preference dimen- sions, leading to improved recommendation performance and more persuasive rationales. Further- more, extensive experiments and real-world deployment demonstrate its practical effectiveness and real-world applicability. Although C-APO shows strong performance in both offline benchmarks and real-world deployment, several limitations suggest promising avenues for future work. First, triplet-level dataset construction relies on costly LLM-based coherence scoring, which may limit retraining frequency. To address this, we are developing a lightweight distilled evaluator that ap- proximates the full scorer significantly reduced cost. Second, experiments focus text-only domains. Extending C-APO to multimodal settings-such as video, music, or image-based plat- forms-by incorporating vision audio encoders is natural next step.
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ACKNOWLEDGMENTS
We would like thank SK Telecom for providing the GPU cluster and associated research infras- tructure that enabled this work, as well as for their support in integrating and validating our approach production service.
This work was supported by Institute for Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea governmen((MSIT) (RS-2019-11100753 Artificial In- telligence Graduate School Program (KAIST)). This work was also supported by the National Re- search Foundation of Korea(NRF) grant funded by the Korea governmen((MSIT) (No. RS-2025- 00555621).
Ethics statement This work introduces novel training objective for LLM-based recommenders by integrating both revealed and coherent preferences in unified generative framework. Our method is designed to enhance user-aligned recommendations and transparent rationales without engaging with sensitive attributes user-identifiable data. We foresee potential risks of societal harm or ethical concerns, and confirm full compliance with the ICLR Code of Ethics.
Reproducibility Statement All experimental results fully reproducible. To facilitate verification and further research, we provide the complete implementation code (https: github. cww (tppark88/cc-. APO) and a curated dataset constructed using the ChatGPT API and Gemma-3-27BB
The dataset comprises structured user purchase histories, candidate item sets, labeled positives and negatives, LLM- -LM-ggnerated rationales, and coherence scores across multiple do- mains. It was produced through resource-intensin inference pipelines and rigorous human curation. Detailed hyperparameter settings are reported in Appendix A
GenAl Usage Disclosure The authors used Generative AI (GenAl) tools solely for the purpose of correcting minor grammatical errors the manuscript. No part of the research process- incluuing problem formulation, data analysis, model design, coding, experimentation, or interpretation of results was conducted using GenAl tools. addition, GenAl assistance was used in the actual writing or generation of content for this paper, aside from grammar refinement.
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Appendix
A REPRODUCIBILITY AND IMPLEMENTATION DETAILS
Code and Dataset All experiments were run on two NVIDIA H100 GPUs. To support repro- ducibility, the implementation code is available at the anonymous links provided. We also re- lease dataset constructed using the ChatGPT API and Gemma-3-273-1 structured user purchase histories, candidate item lists, chosen and rejected items, LLM -eMeeeeerteed rationales, and coherence scores across all domains. This dataset was developed through resource- intensive and costly LLM inference pipelines, coupled with extensive human curation, and is pub- licly released to facilitate further progress in explainable recommendation research. It comprises three subsets: training set for supervised fine-tuning (SFT), training set for Conflict-Aware Pref- erence Optimization (C-APO), and test set for evaluating model performance. Although the SFT and C-APO training sets share the same users, they differ in the rationales used, each was gen- erated using a different LLM, thereby yielding two distinct datasets tailored for their respective training objectives.
which includes well-
Code: hhtpps://githib..cmmpppaat
Dataset (Five Domains of Amazon Review Dataset): https:://rtveee.... e. www.coooooooooooooo dri ve/
Folders/1RGG BZJJAAjjKKK httpaarrrnnnnoooddddo.ae. http:///ee....it/ssssssaarrreeee link
Hyperparameters Table lists the hyper- parameters used in all experiments, includ- ing learning rate, batch size, optimizer type, warm-up ratio, gradient clipping. weight de- cay, and the number of epochs. We used Gemma-3 3--------11 4B-it (Team et al.] (025) as the LLM backbone. We first performed pervised fine-tuning (SFT) using only the chosen items. During the SFT stage, we used the same hyperparameter settings as C- APO (Table 4). except for setting the num- ber of epochs to 2 and the batch size to In contrast, C-APO is trained using not only the chosen item but also the two re- jected items jointly. C-APO was trained for 3 epochs with a batch size of 6, using the 8- bit paged Adam optimizer and a cosine learning rate schedule (initial learning rate 5.0 x 10-5 decayed
Table 4: Training hyperparameters for our model
Hyperparameee
Value
Batch
Epochs
Optimizer
Paged Adam (8-bit)
Leanning rate
10 (cosinc schodulc, applicd)
Warn-up ratio
0.05
Gradicat accumulation steps
sequence length
12,000
Weight docay
0.01 (default)
Gradient clipping
(defaultt
LoRA rank (r)
LoRA a
16
LoRA dropout
LoRA target modales
gr, gale-. upr, down-projoctioss bF16
Precision
Gradicnt checkpointing
True
Parallelism
DeepSpeed ZeRO-2
Hardware
2.X NVIDIA H100 80GB GPUS
minimum value). We adopted gradient accumulation with factor of 8, a warm- up ratio of 0.05, and a maximum sequence length of 12k tokens. Training was performed with DeepSpeed ZeRO-2 using 6 mixed precision, gradient checkpointing. Parameter-efficient fine- tuning (PEFT) was conducted via LoRA (Hu et al.] (022) with r = 8, a = 16, and dropout 0, applied to the v-, q-, o-, gate- up-, and down- projection modules. We searched over B 0.51, 1, 2, 5} and report the best test performance. We ultimately set B = 1 for most domains based on validation performance. To calibrate the coherence scores generated by the LLM, we employed SBERT (Reimers & Gurevych, 2019) as the text encoder (109M parameters; perttencettaasttommrrr//l-------
Evaluation Protocol We follow a standard lcave-one-out protocol: for each user sequence, the last item is used for test, the second-to-lad for validation, and the rest for train. At evaluation time, the candidate set consists of the ground-truth chosen item paired with unseen 29 rejected items, and this candidate list is inserted into the prompt for inference. For performance evaluation (e.g., HR @ k), we set the decoding temperature to 0.7 and, when k = 5, generate 5 samples per prompt.
B DETAILS OF LLM-BASED RECOMMENDATION BASELINES
For collaborative filtering- based recommendation models (CF-Rec), we implemented all methods using the RecBole framework (Zhao et al. 2022 The LLM-based recommendation baselines are described in detail below.
httpp::/ www.bccooooo.... RJCAI Box RecHole
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2024) performs instruction tuning using paired recommendation items and rationales, showing that this approach improves recommendation quality in both zero-shot and fine-tuning settings. We implemented this model from scratch.
Rec-SAVER (T'a et al.
S-DPO (Chen et al. 2024) introduces a ranking--riien oriented training objective for LLM-based recom- mendation, extending DPO with a softmax formulation that accommodates multiple negative sam- ples. This approach improves the model's s ability to differentiate user-pu seer-preferred items and strength- ens the impact of hard negative signals during optimization. This model was developed entirely from scratch by our team.
Intuitor (Zhao et al. 2025) introduces reinforcement learning framework that eliminates the need for external rewards by leveraging the model's own prediction confidence-refenee to as self-certainty-as the training signal. Based on the Group Relative Policy Optimization (GRPO) paradigm, Intuitor enables large language models to optimize their behavior using intrinsic feed- back, without relying on labeled supervision. We implemented the model with reference to the official code repository
Rec-LM (Lu et al. 2024) improves LLMs' instruction-following ability in recommendation by combining supervised training recommender-labeled tasks with reinforcement learning align- ment procedure (Proximal Policy Optimization; PPO), enhancing response controllability without sacrificing recommendation performance. Our implementation was guided by the official repository provided by the authors
(Lin et al. 2025) applies the GRPO framework to the recommendation task, where the reward defined based on whether the generated item matches the ground truth. In our imple- mentation, extend this framework by incorporating additional reward signals from collaborative filtering models such as SASRec. We implemented this model based on the repository that provides GRPO implementation
Rec-R1
GRAM (Lee et al. 2025) formulates recommendation as a text-to-text generation task and in- troduces two key modules to overcome limitations capturing item relationships and handling verbose item descriptions. Specifically, it translates semantic and collaborative signals into lexical tokens via semantic-to-lexical translation module, and fuses multi multi-granular item prompts at the decoding stage through multi-granulas late fusion mechanism. We implemented the model with reference the official code repository
SLMRec (Xu et al. 2024) investigates the redundancy of deep layers in LLMs for sequential recommendation and proposes a distilled small language model optimized for efficiency. Through extensive experiments, the authors find that many intermediate LLM layers are unnecessary for performance, and design a compact model that retains effectiveness via simple knowledge distil- lation. We referred to the official implementation when reproducing the model ? Due to con- straints in the official implementation, we employed a LLaMA-series backbone. Specifically, we used meta-1 ama/ L1 amaa3.m33... 3.23BB, which is comparable in scale to Gemma-3-4B-25 the backbone adopted in our main experiments
LLMEmb (Liu et al. 2025) leverages LLMs generate item embeddings for sequential rec- ommendation, addressing the long-tail problem. It combines Supervised Contrastive Fine-Tuning (SCFT) with Recommendation Adaptation Training (RAT) to align embeddings with collaborative signals. We based our implementation on the official codebase released by the authors
SumRecpro Tajiri & Inabal 2025) is trained via Direct Preference Optimization (DPO) using I.LM-generated dialogue summaries and item recommendation information. While the original setting targets multi-turn conversational recommendation, we adapted the model to our single-turn sequential recommendation task by applying both SFT and DPO accordingly. Our implementation was done from scratch.
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httess//attibb.ccmm//llee/////
httea///aiftabb.cooo////aaana MRec
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C SUPERVISED FINE-TUNING PRIOR TO C-APO
Training Modern LLM-based recommendation systems frequently employ supervised fine-tuning
(SFT) to improve their task-specific capabilities. This process typically consists of two phases: (1) transforming recommendation datasets into textual prompt-response formats, and (2) adapting the LLM using these structured pairs. In the data preparation step, prompt zu, is created for each user u, which includes their interaction history S... a candidate item set C, and an instruction specifying the sequential recommendation task. This prompt is paired with an output ye. which contains the ti- tle of the selected item & E C' and its corresponding rationale Te, forming the training example (zu, Uc). Roughly 70% of these SFT training instances are reused in the later C-APO stage, where addi- tional information such as rejected items, their rationales, and coherence scores is incorporated to model user preferences more effectively. the second phase, the LLM-based recommendation model fe is fine-tuned using the constructed pairs (Zu. Mc). with training guided by causal language modeling loss. This objective, widely used in language modeling, encourages the model to generate the expected output by predicting each token sequentially given the previous context, effectively casting recommendation as a next-token prediction problem. Formally, the objective of optimizing the LL-M-based recommender So with pair data (Iius Me) can be formulated as:
Mel lo (N))
Σ
max8
(zu.Be)
where 1yc| is the number of tokens in ycs (Ue): is the 1-th token of ye and (Ve) is the tokens preceding (Ve)..
However, recommendation tasks are essentially user preference alignment tasks, as formalized in the above task formulation, and differ from language modeling tasks that consider only chosen item. Such a gap necessitates further exploration into aligning LLM-based recommenders with user preference, an area that has been underexplored.
Prompt Example The prompt used for SFT is designed to encourage the model to select the most appropriate item from list of recommendation candidates, given user's purchase history, and to generate a rationale explaining the recommendation.
#Prompé Zu.
Based on [Purchuse flistory), use your logical reasoning process to identify the must suituble item for this customer from the [Candidate List). Then, include your ressoning inside the <chink?> </thi sttg and the recommended item inside the tmm </ /eittennnii tig.
[Purchase History] (1) Finejo
ludies 3/4 sleeve neck husiness bodycon drass, yellow, xx-large Title Farktop women's long slevees digina grafliii printed prom party maxi long dress with bell... ferumcatt -- Sui
[Candidate List): (omitted for bravit)
#Response Uel
<think customer his previonely purchosed two dresaes- business bodycon dress and a long maxi dress... (frunestel) e/thinkk> <iten>>Fibo Fibo steel 10 women black velvet choker neckluve for girls lace choker tatio necklace </ 2/1tomm>
D TRIPLET RATIONALE DATASET CONSTRUCTION RECIPE
As described in Section 2.2 we construct training data in the form of = (i,r, where each sample y consists of chosen item, two rejected items, and their corresponding rationales r and coherence scores 8 with respect to the user's interaction history. These are generated using a state- of-the-art (SOTA) LLM, based on the relevance and logical consistency between each item and the user's history. Note that the chosen item is not guaranteed to exhibit higher coherence than the We detail the data construction recipe and the subsequent human
rejected items, as shown in Fig. D cross-checking procedure to validate its quality.
Stepl: Rationales Generation and Evaluation We first prompted the state-of-the-art LLM 1temma-3-27b-- to generate post-hoc rationales separately for each of the three items-the the chosen item and two rejected (i.c., non-chosen) items-based on their semantic relevance to the user's interaction history. For this tasks, we used a deterministic setup with temperature=0,, follow- ing common practice in LLM-as----udde because coherence scores act as supervisory signals; introducing stochasticity would inject noise di- rectly into the optimization objective and undermine training stability. The following is an example of the prompt used to generate rationales and coherence score.
literature. We intentionally adopt this conservative setting
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#Prempe
iSyseem Prompt) You are recommendirion expert tacked with genentting rationale for the next iee purchssed customer, hased on their price behovior
Your explanation should grounded the relationship beween customer's previous
the ite.. The will purchase (Next Item] from the candidate ttems based their given purchase tissory. Based this, please follow the instruction below.
|Purchase History] (1) Tale: Bai coceout flavored wates, cocofasions varieyy pack (2) Tale: Organic caconut otl...ttuuncteed) i- S
[Candidate List): (omitsed for brevity)
(Next Item] Tirle Wild planet albaoore tuno with stit, canned tunn, son-gooo, kosher bo S/.
Unstruction) Step by step. explain why the customer will purchase the [Next Item based on its (Parchase Historyl. Describe it in a way that, result of the rocammendad. particulla, deriving the csssoning process carcflly miiing one Tamiile Bad, (kay, Good, Excellemt-or parchased nom. along with associated review. Analyze both the positive and negative reveews thonughly.
ressening process,
[Purchase History) which includes
Think ahout the logical relevanee (ceasoning process) between the customer's [Parchase History] and the [Nest Ite]] Also, evalunte the logical coherenee customer's |Purchase History] and the |Nest Item]
from I (:: Weak- No connection, Weak Poedy justified 3: Slightly relecance, 4: Nautral Ambiguous marginally related, S: Slighlyy Strong- Generally aligned, Strong: Well alignal and logical Very Strong- Highly eoherent and comeestully perfect), and insedt the seare within the <Co i mng // 2nggstag Be critical, not generous.
For example, write it like this:
<Chiink 2-your reesoning process < //hhink' > <reting degree of the logical osherence </rating>> K1.
Step2: Human Evaluation Protocol
After generating the recommendation rationales r and their corresponding coherence scores from the LLM, we conducted a statistical validation to assess whether human evaluators make similar judgments regarding the quality of as those reflected in s. If the agreement is not statisti- cally significant, we return Step I and revise the gener- ation process. Specifically, seven domain experts in the field of recommender systems evaluated approximately 400 samples from the LLM-generated dataset by assign- ing a coherence score on 1-7 Likert scale, following the same instructions provided to the LLM (See the above prompt). The scores assigned by the experts were subse- quently aggregated to obtain the mean human-evaluated coherence score per sample. We then performed a Spear-
1
1
4
0
0
-70
2
11
28
0
50
LLM LLM Score
3
0
24
27
4
0
0
2
19
26
38
0
19
0
0
8
16
39
2
20
6
5
21
7
0
0
1
5
1
4 5
Human Score
Figure 8: Confusion matrix between
LLM and Human Coherence Score man's rank correlation test between the LLM-geenratte coherence scores and the human ratings, obtaining a statistically significant correlation coefficient of 0.71, < 0.0001. Additionally, the Quadratic Weighted Kappa (QWK) score was 0.63, indicating substantial agreement between the model-generated scores and human evaluations. To further visualize the distribution of agree- ment between human and LLM assessments, we present the confusion matrix in Fig. & This result indicates a substantial agreement between LLM assessments and human intuition, supporting the reliability of the coherence score as a meaningful training signal.
Table 5: Rationale Quality
Step3: Data Filtering Criteria Subsequently, for training data con- struction, we filtered out all instances where the two rejected items received identical coherence scores. As a result, our final dataset consists exclusively of triplets the form of (chosen, hard rejected, easy rejected), where the hard rejected item is guaranteed to have higher coherence score than the easy rejected item. This filtering step preserved approximately 70% of the original data instances.
History Rationale Quality Length count mean std
998
2.20
0.81
3-7
538
2.68
0.82
8-11
142
2.70
0.81
12+
220
2.76
0.71
We additionally conducted an experiment in which we generated rationales for users with only 1-2 interactions (sampled from approximately 2,000 instances in the raw Amazon review data) and evaluated their quality using the same 0-3 protocol described in Section 4.4 Histories of length 1-2 produced substantially lower-quality rationales, as shown in Table
'A total of seven domain experts participated in the evaluation: one service marketer (female, age 34); two service engineers (male, ages and 29); and four data scientists (male, ages 37, 33, 31, and 30). This group reflects diverse professional roles spanning product, engineering, and modeling. All annotators scored a shared subset of items, enabling measurement of inter-annotator agreement. For cases where annotators' scores differed by more than 4 points on the 7-point scale, consensus procedure was conducted to resolve discrepancies.
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(a) Ground Truth Ittm
(b) Hard Rejected
(c) Easy Rejected
(atio(%)
40
Weak Coherence
Weak Coherence 40%
Weak Coherance
30%
85%
20
5
1
Figure 9: Distribution of coherence scores (LLM-as-a-Judge) for ground-truth (Chosen), hard re- jected, and easy rejected items the Amazon dataset, computed from the relationship between each item and the user's ' interaction history (1-7 scale, higher more logical). Chosen items generally distributed in the higher score range, while hard and easy rejected items cluster in lower ranges.
E PROMPT TEMPLATE FOR C-APO
As described in Appendix D after constructing the training dataset, we first performed supervised fine-tuning, followed by reinforcement learning based on the C-APO framework. The training prompt includes the user's purchase/view history, a list of candidate items, and an instruction. The model tasked with generating a recommended item- either a chosen, hard rejected, or easy re- jected item- aicomaaccoppaneed by rationale for each. For comparison, our experiments configure DPO to use a single randomly selected negative item (either hard- or easy rejected), whereas S- DPO designed model multiple negative items leverages the entire triplet structure comprising the chosen, hard rejected, and easy rejected items, as does our method. An illustrative example of such prompt is presented below.
#Prumpl
Based [Purchase History], use logical reasoning
ideniify the most suitable item for this customer from the (Candidate List). Then, include ink naag the resommended inside the < ten
your ressoning inside
[Purchuse History] (1) Tide: Diamond crystal koober sall faaees (ull flavor, no additives and less sodium (2) Tille: Secos organic cinnsmon powder, cincumon, grund cinnamon for coffee, baking and cookingg....nnnn (rrunccdd (+ Su
[Candidate List): Comitsed for brevity)
#Respose Vei
<think>-The custemer has democstracd preference for flavariul staples specifically, gourmet salts, spices, seas... (truncated) < </taink>>
<itten-Nature
burs, sally salted caramel chocolate < </iten>>
#Response Vi
<think>> The custemer has demoestraed preference for puntry staples and Baaoring agents, purchasing Diamond... (inuncated) </shink>>
<iten:-Wait lily sell rising bleached four 5lb </iten>>
#Response We
<think > The customer has denvestrated preference for pantry staples and slavorfl additions food, parchasing both.. (trnncotdd) </think>> added variety fruit cups </iten>>
<itan> Del monte
As mentioned earlier, this prompt is shared across all models DPO (one rejected item), S-DPO (two rejected items, and the PL model (two rejected items) differing only in the number of re- sponses used.
F RATIONALE EVALUATION DETAILS
We describe our evaluation procedure for LLM-generated rationales, as introduced in Section 4.4 First, we tasked the SOTA LLM itself (i.e., gpt 40 series) with scoring the coherence of each generated rationale according to a predefined rubric. Importantly, the LLM used for this scoring step distinct from the one employed generate the rationales during dataset construction, ensuring an independent evaluation. To assess the reliability of these automatic evaluations, a subset of samples was independently annotated by human judges, who followed the same evaluation criteria provided to the LLM. We then conducted statistical tests examine the degree of alignment between LLM- and human -asianedd scores, and found a statistically significant positive correlation suppiioinn the validity of the LLM-based rationale assessment.
Rationale Evaluation Prompt prompting a state-of-the-aar LLM to evaluate the rationales on a four- court scale, using the following prompt.
We assess the plausibility of the generated recommendations by
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*e*Prompt for the Rationales Evaluation**
Innttruction
Please net as neutral evalustor assess assistant's recommendation its parionales based on the user's purchase history, the predicted item, and the ground truth item. Use the following four-level scale assign your rating:
RATING-O- Incorrect recommendation The assistant fails to recommend the correct item.
RATING-E:
Correct recommendation, poor explanation While the
recommends
item, explanation missing,
vague, olf-topic, or inclades ballucinated content that doesa'Lalige align with the actual context.
RATING 2: Carrect recommendatiion reasonable explanation
assistuni offees recommendation fits
profile
provides logically coherent explanation. However, the explanation may still lack depth, clarity, or persuasive detail.
RATING-3: Conrect rocommendation. strong explanation The assistant not only recommends the right item but also presents a clear and insightful explanation. referencing the user's behavior patierns and showing how they relate the suggested item.
Please submit your evaluation using the format <eeva RATING- </eval>> 1>, for instance: <eva Develllee 1 </eval>>. Avoid letting the explanation length affect your judgment
objective assesmmnnt
#Known Information
Fansing costume jewelry fathers day gift punk screw carrings (2) lblue jewelty stainless pairs mens black gold silver stud carrings (tuncatt)))))
[Purchase history] (1)
IRecommended [tem] Jstyle stainless stoel mens womens stud carring hoop carrings for men pairs
[Ground-Trus Truth Item] Jstyle stainless steel womens stud earring hoop carrings for men 7 pains
[Rationale] The customer proviously purchased two sets of stainless steel stud carrings- - one with a punk aesthetic and... (truncated)
Agreement Analysis between LLM and Human Rat- ings To validate the LLM-derived ratings of ratio- nale quality, we asked two domain experts to evalu- ate about 250 samples drawn from a total 1,500 in- stances (Section 4.4) To assess the agreement between
0-
0
0
0
0
375
150
LLM Rating
7
1
1
0
125
LLM-generated and -asian- scores, we report
100
Quadratic Weighted Kappa (QWK 0.75, p 0.0001) and Spearman's 's rank correlation (p = 0.63, P < 0.0001).
0
4
15
16
Both metrics indicate strong agreement: the QWK score reflects substantial concordance while accounting for the ordinal scale and penalizing large discrepancies, whereas Spearman's captures the consistency in ranking and similarly demonstrates high aligmment. The results in- dicate a moderate level of agreement in absolute scoring and a fairly consistent ranking of item quality, support- ing the validity of the L..M-based ratings and their alignment with human evaluations. To further examine how ratings differ across score levels, we visualize the confusion matrix between human and LLM scores [O). Most predictions are concentrated along the diagonal, indicating that the LLM generally assigns scores close to human judgments, with only a small proportion of large discrepancies.
3
0
0
12
193
25
0
3
Human Rating
Figure 10: Confusion matrix between
LLM and Human Rationale Rating
Case Studies of Rationales To better illustrate the qualitative differences in recommendation gen- eration across models, we provide representative examples in Fig 11 and Fig T2 comparing our method against several competitive baselines.
Our model consistently generates rationales that are not only more fluent but also more logically grounded in the user's actual purchase history. For example, in Case 1 [T) our method iden- tifies the user's strong preference for women's 's ommends women's cropped cardigan that naturally complements those items. In contrast, Rec- SAVER infers a general preference for practicality and suggests men's 's jeans, overlooking the user's clear pattern of purchasing feminine apparel. S-DPO, on the other hand, focuses on the user's ap- preciation for expressive and practical items but recommends a humorous graphic t-shirt, which mismatches the more formal tone of previous purchases. These comparisons demonstrate that our model offers recommendations and rationales that are more coherent, contextually appropriate, and better aligned with the user's underlying preferences.
dresses and skirts-and rec-
Another representative example is illustrated in Fig. 12 Our model identifies clear pattern of preference for durable, outdoor-ready products, such as Casio G-Shock watches and Carhartt work pants, and recommends Timberland waterproof hiking boots that naturally extend this theme through similar emphasis on ruggedness and functionality. In contrast, Rec-SAVER captures a general in- terest in utility but recommends moisture- moisture-wicking socks, which, while practical, lack the salience and robustness of prior purchases. S-DPO focuses on the user's 's appreciation for high-performance accessories but similarly suggests socks, overlooking the broader pattern of activewear and heavy-
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duty gear. These comparisons highlight that method yields recommendations and rationales that are more coherent, contextually grounded, and better aligned with user preferences.
Prompt
Based on the [Purchase History),, user your logical reasoning process to Identity the most suitable item for this oustomer from the [Candidate List), Then, include your reasoning process inside the (think) your reasoning (/think) tag recommanded
the (ttmm nm? Item titte </ itemm (emm_nm) tag.
Purchase History
ban wotner's thase front open curdinn skouws inagulal hoom
Item 10: 907.020XV5
Description Datall: "Department" "Wrmmnn" "Dane Auntaaaa""" "Rugunt 2010
Tite: Dearcase women's aing sleowe mued crese crewmeck loos plain casusi arr pire want long dresses with pockata
Hown iD: 9078NS1
Description slince.
long
green nayy
Rading Fecelient
(Tuncorad))
Tatla sthben roon worenn's rtylich spardes comdyy foldoower fivr longmaddi
Reviow: Vary nice material and cxtara. looouht frizy I-e pattom ea arretler but blach Mlu Rowellter far Bestif tadter like 89 Rading Focelim
Candidate List
jumitted traxity)
Generated Output
C-APO
Rationalo
The customerr has shows preference cloening particulbrly dresses evidenoed their mail evoslient
saristaction style,
suggesting customer tachionable yet euggett
practical dlathing
cropped cartigan, aligns perfectly offering almery
giggen open design apparal, highty cantigan lag <ittink> previcus strungly supports lootihood
purchee
cardigen
card
Rec SAVER
Rationals]
17 The customer hos shown olcar proforencc for women's clothing homs, port lor'y those that are conffortable natte and verazdiic,
nood by their purchases
maxi drosses and skirts all roted escctient The revlows indicate sartddadd on with the quality and fit of those home, suggest ng that the ousbomer solues groctibol
style their wordrobe cholons. Our rncommended hom, the Leviss mon's s 301 original itt jeans, aligns with this trend of sooking rollable and comfortable clothing. even
though It men's hom. The custo ner may appreciate ane the classio style and versatilit of Lwv's jeans, dimiter to how they found value in their previous purchases.
Addtiionnlly the jeans am described as having misaed which-co could appwal the customer's desire for comtor.. Thus, desptts bwing diffeeent categgry, the choice ieflects a beowder interned quality spparsl that suis their lilestyle Oossall, the lngital connaction lies in the oustomen's setiellthed preferenze for mnrwel-mmdd.
commoortatte ciothin cthirkk>
rencoommeneettt Item
< Mlami Junp Loo mara 501 origina It juans eitiemnnnp
SDPO
Rationataj
The
preference wormen's's clothing
comfortatte, Indicate satietsction with and aeathetic mocomended hem, humorous guphic hahit, aiggt this teed of seeking and regrosaue clathing purchused hame that nelect penennlity auch calorful and diesses. The funny natur stort may reanratt
evidenced their purchases these items suggneting the
masi dieesss skirts euzallant,
customer valuwa feshinmable hunchonw
Adsttionlly the customer has with cuplomer's sersee humes, making appasling canual warr option. Cownall comfamation style, comfor, and pansoral expresoiio makass the i-shirt Ishun the contumer. <thinks
Ilspommended Itam)
<tengimm Funnly cill nignnors ta a oray chil erginnning
t shint sibe__mmm
Figure 11: Different rationale qualities across models for the same prompt (Case 1)
G SERENDIPITY ANALYSIS
Because C-APO increases the likelihood of history-consistent items, concern is that it may over-anchor on past behaviors and reduce exploratory recommendations. To assess this, we analyze serendipity using the standard metric Serendipity(uu (u) = [R.DT.PI where Rg are recommendations. T, true interactions, and P globally popular items. Empirically, C-APO achieves a serendipity score of 0.30 versus 0.25 for the SFT-only model, indicating that recommends more relevant yet non-popular items rather than collapsing into echo-chamber pattern. Moreover, shown in Fig. 6) moderate C-APO alignment (c.g., 3 < 1) provides a favorable balance between accuracy and exploratory diversity.
RR..
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Prompt
Based the [Purchase History), your logleal reasaning process identifty suitable Item customer from [Candidate Ustt) Then, Include your reasoning process the <think) your roasoning process (/think) tag and the recommonded title inside the <itom_ rm) item title </ ( tag.
pe-75000 wetch black Item ID: 500oorCiio
Description & Detail:: V the nover unding pursuit toughteces and technologe C Shoek rtrocueca soar atomic addit on the tough and "agged Reacue aeacuuaartien Restowt band the witch hhoss our about 10 monthe. Ditent worr in every dwayydyyy for ather abous montha conetarre waar
Ruting Encelaat
Title Cheii man't ga 1p-4 or master ntain atmel roterwatch
Item ID: DODGEYYRLA
Deseriptio Detata: Casio Rongumen 0W94400 AB mis Twin Bersor Whisswawtoh.Case..
Roviow soee
Rading
(hincaned)
Title CCarham man's
dusk double from atti work part
Ihem ID: DotoGRu2H4
Deseription Detais Dosikke un durob lts with can enths double fron work dungarie in 12 surrce fi hand, 100 peroort tiing spur oution dool, fictures chap-styy doublo Insnrt parnits well double trnes with cleanout bettoors accommecane emee prids tough
pockarta camyy my gear. rgorous
years.
Rating: Excetient
[Candidate Lins (rmmitted for beawity)
Generated Output
C-APO
Rationale
<hip The oustomer has ounly purchased multiple Casio watches, spooifooaly models known for thei durability and functionality such as the O Shock and Rongemann weagoraawwtthhee es, both rated 25 cxcellent. These watches cote a profe for rugaed, rejabto aroducts con withostand harsh can dizons, suggosttng that the customor volues quality and perfomance in their purohases, Our aocemmended item the Timbo land men's white ledge mis waterproof hiking boot. aligns porfectly with thrs preference, as emphastoes Sumblity and water pettrrpooofig essential tritt appresiated in soooo actheties. Addttionlly, the customer hat shown inte est cundoor and functionna ciothing as esédenced by thair purchose the Carhort work pants, which slso hightight proticality and reallannos The Timberaand booss complement this trand by offering soldd constn ittion suited for owrleue termins, reintorsing the customer's inciliation rowerdd dumbte tnchwwerr Tharsfoee, the lngital conoection betweer the costoreers paol purchares and
item indirattes in strong (leelinaed of purchweongg the Timoertend tiking brccs, <think
[llcommandees Ihem
<llam Jamp
while lodgo mid waterproof hiking <itiemnnno
Rec-SAVER
The
dursbis
particularly designed activties,
Carhart pants,
The Carhort indicating
digning
Addiiiontll
reliable
moisture customer's previous poshiiv experlances high performence appardt settings. Thersfors, the decision purchase the Dickies anclis partectly with entablished preferen for functionality their purchanes, </hink>
quality funciiooality. The Dickis physical activties.
Itlpack momleme
offering
comfoot. featu
cal that choose another Item enhar thelr performance during
[Incommenden Item!
Slem pinr Declowe menk -tach muberue control wodie mullipacc +iamm rmn>
SDPO
[atioonta
<PiPke The custonecr Pos previously purchased multiple Cosio watches Indicating soong proforenee durozte, rugged, tochnoagiclly odvanced products,
wetches received ratings posithve reviows, hightig vinn their toughness, funesonaliiyy ense reacaabiltyy Our recommee sture control socks muttipace, sligns customer practiccal approach surchasing quality they Additioonaly aocks designed comtort performance, reconatee customer's ctuibblle buying pattem.
hom, the DicMes mon's
nd daily
apprec
qualry materiats funchionwityy Engical
to purchsse anotwer pair of
reputable
mullspace <itmmnmm>
Figure 12: Different rationale qualities across models for the same prompt (Case 2)
H EXPERIMENTS ON OTHER DOMAINS
H.1 COLDD-TART AND ZERO-SHOT DOMAIN TRANSFER
LLM-based recommender systems (LLM-Rec) have been shown to exhibit strong robustness in cold-start scenarios, often outperforming traditional collaborative filtering recommenders (Lei et al., 2024). Because LLMs encode broad semantic knowledge, they can also provide reasonable pre- dictions for domains that were never observed during training-a capability that CF-based models typically lack. To evaluate this aspect, we examined the zero-shot domain transfer ability of our approach (Ta
Table 6: Performance Comparison on Unseen Domains
In this experiment, the CF-Rec baselines were trained directly on the target (unseen) domains, whereas our model was evaluated without any domain-specific supervision. We considered two representative target domains: Amazon Beauty and CDs Vinyl. For our model, we selected semanti- cally related source domains for training: Amazon Fashion for Amazon Beauty, and Musical Instru- ments for CDs & Vinyl. The results show that, de- spite not being trained on these target domains, our model achieves performance comparable to CF-Rec
Domain
Amazon Beauty
CDs and Vinyl
Models
HR@ 1 HR@S
N
HR &
HRG5
N
SASRec
2.90
16.12
9.18
3.26
16.34
9.61
BERT4Rec
3.26
15.04
9,01
3.14
14.96
8.89
GRU4Rec
3.26
17.57
10.51
4.29
24.00
13.73
FOSSIL.
1.81
13.95
7.84
3.72
15.74
9.59
NextliNet
2.72
14.67
8.45
3.68
15.65
9.57
TransRec
10.33
2.93
14.90
8.69
SINE
3.80
15.04
9.26
4.64
18.04
11.23
Rec-SAVER
8.56
31.51
19.69
10.16
27.84
19.40
Ours (Unseen)
19.49
40.62
30.81
10.08
28.44
19.66
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Published as a conference paper at ICLR 2026
models trained explicitly the unseen domains. This demonstrates that C-APO effectively captures user behavioral patterns and item semantics a manner that generalizes well across domains, high- lighting its robustness in cold-start and unseen-domain scenarios.
H.2 EXPERIMENTS ON NON-AMAZON DOMAINS (MOVIELENS)
To verify that C-APO not limited to Amazon-style review data, we additionally evaluated the model on the MovieLens dataset, which differs substan- tially in structure despite also be- ing text-based. Unlike Amazon reviews that contain rich free-form descriptions, Movielens pro- vides only short user-generated tags, making a fundamentally different type textual signal.
Table 7: Performance Comparison the MovieLens Dataset
Model
BERT4Red GRU4Rec NextlNNet
SINE
STAMP
Ours
HR @1
2.26
2.51
2.25
1.93
2.08
2.81
HR@5
12.84
13.43
10.41
11.60
9.86
13.13
NDCG@5
7.38
7.86
6.24
6.61
5.88
8.10
Even under this distinct data structure, C-APO consistently outperformed all baseline models, flecting the same performance trends observed the five Amazon domains (Table These results demonstrate that the core principles of our approach-CP-basne reasoning and conflict-aware op- Limization-generalliz beyond Amazon datasets and remain effective in structurally different do-
mains.
I GRADIENT DERIVATION FOR LC-APO
Setup and notation. Let 9k == g( Uk. be the model reward for item yv given user context Tuy and define
Agh.c 9h Jc>
Age.c Ge Ics Age, -e
gh>
V
Vog Mkk)
The C-APO loss in Eq. cann be written as
CC.APO - E log o log (e Wa.n "e, ABe.)) + log
log @WW.c ) (10)
where (-) is the sigmoid function and We.k We,er Whe > O are the conflict-aware adaptive weights. For clarity in gradient analysis, the trainable layers of the conflict-aware adaptive weight module were excluded from differentiation.
Useful identity.
For z = =(0), we will use
dz
(ogo(-z)==oo =
(1I)
00
00
Therefore, with the leading negative sign in E0 10 each term contributes a factor +z((2) Ogz to the gradient.
First term (RP aligned with CP). Let
2 == log (e We,n Agr.c + e Wo ,g,))) = log S, S: c "enn + t wo,e Age.c.
By the chain rule,
1 Wenn Wa,h Agh.c We,e We, Og DgDge
09 0o5- 5
Since Agh. 9h Io and Age, 9c 9c.
Og Agh. = Vn Ver
On On = V. V
Hence
0ø21 Men Aen.e (V, Vc) We,e Wee (Ve Vc)
(12)
We,.Aan. We.e Age.c
Using Eq. I1 with the outer minus sign in Eq. 10 the first term contributes
We.he Agh.c (V, Vc) + We Wer
Aer Abc.c (Ve.eeee....... Vc)
o 0321 of log(e meen Agrie (e..)
We,h Ath.e We.e Age.c
(13)
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Published as a conference paper at ICLR 2026
Second term (CP).
Since log (e "urr) Whec Ag., define
22 = Wh. Ageh Wheele - 9h.
Then
0822 Whee OgLge. wh,e Vn).
(14)
By the same identity Eq. 11 (with the leading minus in the loss), the second term contributes
o (2) 0222 o wh. Age.h) whe (. VA).
(15)
Final gradient.
Combining Eq. 13 and Eq 15
inside the expectation yields
V Cc.A.O
of log( (oggeemnamm mmad to to ao..)) mase Mea Menddeee Ve) toree ( (V.-V.) of afws, who (V. Val
E
for
of log(e logge "oaeenn denr)))))) mae ee Wonee we)tw Mead Meadse
ofv wao (Va V.)
(16)
which corresponds exactly to Eq. presented in the main paper.
PROBABILISTIC INTERPRETATION OF THE PAIRWISE WEIGHTS
We provide a generative interpretation for the conflict-aware adaptive weights wi, used in our ob- jective. Let the latent utility of item E fc, h, e) be
U, (us, o
where (14; ō;) are (calibrated) parameters inferred from the text-encoder features of the user-item- rationale tuple. Then, for any pair (i,j),
wiy = Pr[u U,] Pr - U, 0j ②
14i 14 03
where (--) denotes the standard Gaussian CDF. Therefore, the weight ue. is the pairwise win prob- model of noisy utilities (Thurstone 2017 Mosteller model is a classical instance of the random utility framework, in
ability of over under 1951). The Thurstone-Mostelle
which the utility of each alternative in a pairwise comparison is modeled as a latent score perturbed by Gaussian noise. This model has been widely adopted as foundational approach for modeling pairwise preferences and ranking data across various domains, including social sciences, psychol- ogy, and recommender system.
Substituting wi,j into our loss yields a probubility-weighter variant of the PL/DPO family which pairwise gaps (g, 9j) are modulated by their estimated reliability.
Consequence. When (p: - M4)) is large relative the joint uncertainty of -53. the model assigns wa & 1, amplifying the update in the direction that promotes i > j; when the pair is uncertain contradictory, wij & 1. attenuating the update; and when (wi M4;) is negative, wisi approaches O, thereby weakening the update that favors > j.
K BOUNDARY CASES AND REDUCTIONS
We collect simple but instructive reductions of our objective.
Lemma (Reduction to Plackett-Luce (PL) model). ff = forrr all pairs, CCAPO (Eq. b) reduces exactly the PI. objective that jointly enforces ye yh. Ye via two-stage factorization.
23

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Published as a conference paper at ICLR 2026
Lemma 2 (RP-only limit). If the CP comparison is disabled by setting wh.e 0 for by dropping the CP term) and We.ks We.e = 1, LC-APO reduces to the S-DPO objective (Chen et al. 2024) that promotes Ye above {yh. Ye).
Lemma 3 (Temperature rescaling). Ifwi,j = (0, oo) is constant, then LC-APO is equivalent to applying lemperature 1/A to all pairwise gups inside the log-sum-exp. This does not change the target ordering but rescales the sharpness of the updates.
Implication. These reductions clarify that our method strictly generalizes standard PL/DPO-style
training: it recovers PL when uncertainty is ignored (w 1), focuses RP when CP is omitted (Whee- 0), and smoothly interpolates update strength via a temperature view when w is constant.
L REFINED COMPLEXITY STATEMENTS
Let CLLM denote the cost of one forward/backward pass of the backbone LLM for single (æz, y), B the batch size, and M = 1 + R the number of candidate responses (one chosen, R rejected; here R=2).
Proposition 1 (Training cost). C-APO evaluates M responses and combines them via two-stage PL. factorization, yielding O M CLIM) per step. For DPO with single pair (c, (c,-), the cost is O CLM).. the calibrator uses lightweight encoder with cost Cene < CLLM, its contribution is negligible to leading order.
Proposition (Inference cost). inference, scoring M candidates costs O(M CuMM; aggrega- tion (e.g., softmax/lo-ssume sumpeexppp over M) is lower order. Thus latency is dominated by backbone evaluation and scales linearly in M for a fixed input length.
C-APO matches the leading-order complexity of PL/DPO-style training while adding only lightweight weighting step: practical latency is governed by the backbone, not by the calibrator.
M DISCUSSION OF SCALING-LAW
We conducted a comprehensive evaluation using backbone models with parameter sizes of 1B -Gemm--3-1Biit), 4B (Gemma-3-4B-ic) and 12B (Genma-3-12B-it) As illustrated in Fig. 13 model performance exhibited consistent upward trend with increased model size across both the Grocery and Gourmer Food and Clothing Shoes and Jewelry domains. All three evalua- tion metrics-HR&@ HR@5, and NDCG@5 NDCG@S-improve monotonically as the number of model parameters increased, reflecting a clear scaling effect in recommendation performance.
Despite these gains, increasing model size also leads to higher inference latency, which can be limiting factor real-time deployment scenarios. To strike balance between performance and responsiveness, we adopt the 4B parameter model for integration into our production environment.
NDCG@8
(a) Grocery and
Clathing
Jewelry
25
25
Performance
Performantt anformannee(S
15
15
10
10
5-
0
128
128
Backbore
Figure 13:
Performance of C-APO improves monotonically with larger LLM backbones
1ommma-3-1B/44-0 confirming a scaling-law trend across two domains.
24


Reviews:
[{"review_id": "2H6Xz9xrmR", "reviewer": "Reviewer_XFSC", "summary": "Traditional recommender systems, including those using LLMs, rely on Revealed Preference (RP), assuming user actions reflect true interests. However, user behavior is often noisy and inconsistent, leading to flawed recommendations and unpersuasive rationales.\n\nThis paper introduces Coherent Preference (CP), a concept prioritizing items that are logically and causally consistent with a user's entire interaction history, not just isolated choices.\n\nBuilding on this, the authors propose Conflict-Aware Direct Preference Optimization (C-APO), an LLM-based framework that jointly optimizes for both RP and CP. C-APO uses a unified ranking system that combines signals from both preferences. Critically, it adaptively reconciles agreements and conflicts between RP and CP, allowing it to better capture genuine user intent.\n\nOn the Amazon Review dataset, C-APO outperformed approximately 20 state-of-the-art baselines in both recommendation performance and rationale quality. A real-world deployment confirmed its practical effectiveness.", "questions": "Regarding line 242, how is the conflict-aware reward weight w_{i,j} defined? Is it a function with g_i - g_j as input? Is it learned, or is it predefined as a constant?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "The idea proposed in this paper is easy to understand, direct, and effective. However, the cost of dataset construction might need to be considered.\n\nThe paper provides some theoretical analysis and proofs.\n\nThe experiments are very thorough, for example, comparing against as many as 20 baselines and using datasets from multiple domains. The experimental results also demonstrate the effectiveness of the proposed model.\n\nThe code and dataset are publicly available.", "weaknesses": "Figure 2 and the paragraph that references it (i.e., the paragraph starting around line 048) do not seem consistent. I could not fully understand what the authors were trying to illustrate with this paragraph and Figure 2.\n\nThe paper might be somewhat difficult for readers who are not very familiar with related work. For instance, some terms like \"rejected item\" lack an intuitive explanation when they first appear.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "40VUbyF2Z0", "reviewer": "Reviewer_EkyG", "summary": "The paper argues that RP (using observed choices) is often noisy and proposes CP: ranking items by their logical/causal consistency with a user’s history (measured via LLM-generated rationales and 7 “coherence” scores). It introduces C-APO, which extends a DPO objective to a triplet ordering (chosen, hard-rejected, asy-rejected) and reweights updates based on RP-CP agreement/conflict using a SBERT-based calibration module. Experiments on multiple Amazon domains show gains over a varietyo f baselines and an online A/B test reports significant CTR gains over a production ML model.", "questions": "- Please see the weaknesses section.\n- Were the annotation guidelines and instruction provided for the annotators disclosed? How many annotators were involved? Were they diverse in terms of demographics? What was their background in annotation? Additionally, what measures were taken to validate the quality and consistency of the annotations, and ensure the reliability of the labeled data?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- Clear motivation adn evidence of RP -CP conflict \n- Principled objective unifying triplet ordering with adaptive weightswith transparent gradient view.\n- Thorough empirical sweep across five domains with many baselines, reproducibility steps and code/data link.\n- Online A/B provides an initial real-world signal with latency considerations", "weaknesses": "- As acknoledged by the authors, human agreement with LLM coherence is positive but imperfect (ρ=0.71), and the paper itself treats LLM scores as noisy. CP also sometimes ranks a rejected item above the chosen one. This is significant as CP scores directly control learning pressure via weights. Residual mis-scoring can redirect gradients and produce brittle behavior in edge cases (e.g., sparse histories). This does not invalidate results but lowers confidence that gains generalize when the coherence signal weakens. More per-domain noise analyses might mitigate this concern. \n\n- The A/B study shows a large CTR lift with impressions/clicks and a z-test, yet lacks confidence intervals and downstream KPIs. The deployment is a pilot over a 2-month window. For claims of practical utility, reviewers need assurance that lifts are not artifacts of short-term novelty, catalog skew, or traffic allocation quirks, especially since the method changes not only ranking but rationales surfaced to users. Additional transparency would substantiate impact claims. Can you please report CTR CIs, per-user variance, and any downstream metrics (e.g., dwell, purchase, satisfaction proxy). Also clarify any holdout/novelty controls. \n\n- All offline datasets are Amazon text domains. The approach conceptually targets broad recommenders (video, image, music), but no multimodal or cold-start setting is shown. The coherence construct likely depends on modality. Showing at least one non-text domain would strengthen the generality claim.\n\n- The paper shows Base < SFT < DPO/PL < C-APO but does not include “C-APO (no SBERT calibration)” or “PL + calibration (no conflict-aware weighting)”. Without isolating the calibration block (Eq. 7), reviewers cannot attribute improvements to the central claimed idea (conflict-aware weighting) versus the auxiliary scoring stabilizer. This affects interpretability of the contribution and the portability of the method to settings where the calibration model might behave differently. Can you add an ablation for C-APO without the SBERT-gate and/or PL + calibration but no conflict-aware weighting? This would clarify which piece drives the gains.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Wxeg3rfLU9", "reviewer": "Reviewer_VjMo", "summary": "The paper introduces the concept of coherent preference to complement the traditional principle of revealed preference. The paper proposed a LLM-Rec framework that jointly optimizes RP and CP by constructing an ordered preference data for optimization.", "questions": "Please see the previous section.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The approach of inferring intrinsic preferences by reasoning over users’ choices with an LLM is well-motivated, and the mathematical derivation of the optimization is reasonably solid.\n\n2. The authors further propose dynamically adjusting the coherence score to accommodate scenarios with high data noise, where unselected items might actually reflect stronger user interest if available—an aspect often overlooked in existing methods.\n\n3. Extensive and convincing experiments.", "weaknesses": "1. The deployment of the LLM appears to substantially increase computational cost, especially when the user interaction history is long. It is mentioned that the LLM is prompted to output a rationale for the user’s choice and a coherence score. This suggests that the overall computational cost could be quite high.\n\n2. Regarding the use of an LLM to generate the coherence score, how much previous interaction history is required? The paper should elaborate on how the length of this history affects the quality of the generated rationales and whether having too few interactions could negatively impact performance. This information would be useful for designing a filtering method to select high-quality training data.\n\n3. A readily foreseeable limitation of the work is the potential “echo chamber” effect, where noisy but exploratory user actions may be smoothed out by the model. The authors might consider discussing this issue further and suggesting possible ways to mitigate it.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "nWtcZ6O8Js", "reviewer": "Reviewer_AZEL", "summary": "The author introduces C-APO, a large-language-model-based recommender framework that unifies Revealed Preference (RP)—the user’s actual selections—with a newly defined Coherent Preference (CP) that measures logical and causal consistency with past interactions.", "questions": "Since coherence scores are generated by an LLM, how do the authors ensure consistency across runs? LLM outputs can vary depending on temperature, prompt phrasing, or even random seeds. Were the scores averaged over multiple samples, or was the generation deterministic? Some empirical evidence of score stability would strengthen the paper.\n\nThe SBERT-based calibration step (Eq. 7) is interesting, but it’s not clear how much it actually changes or stabilizes the raw LLM scores.\n\nThe triplet-based data construction is computationally heavy. How practical is it to extend this method to millions of users or large catalogs? Is there any plan for lightweight CP scoring or distillation to make it more scalable?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "The paper presents a very interesting and well-executed idea. I like that it goes beyond the traditional Revealed Preference view and tries to model Coherent Preference, which captures how consistent a user’s choice is with their history. That’s a fresh perspective, and it makes intuitive sense for real-world noisy data.\n\nThe paper doesn’t just show better HR or NDCG—it also includes online A/B testing and a rationale-quality evaluation, which makes the contribution more convincing.", "weaknesses": "My main concern lies in how stable and reliable the LLM-generated coherence scores are. Since large language models are inherently stochastic, the same prompt could give slightly different scores on different runs or across model versions. The paper doesn’t discuss how this variability might affect training consistency or bias.\n\nRelatedly, because the same or similar LLMs are used both to generate the rationales and to train the model, there’s a risk of evaluation bias or circular reasoning. It’s not clear how independent the “LLM-as-a-Judge” step is from the model being optimized. Even though human validation is mentioned, the stability and generalizability of those coherence judgments still need stronger evidence.\n\nThe smaller concern is scalability. The process of generating and scoring rationales for triplets is expensive, and it’s not obvious how feasible it is to apply at industrial scale. Finally, while the authors describe Coherent Preference as “causal,” it’s more of a semantic or logical coherence measure rather than explicit causal reasoning. Clarifying this point would make the conceptual framing more accurate.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)