Title: PANTHER: Generative Pretraining Beyond Language for Sequential User Behavior Modeling
Venue: NeurIPS 2025
Accepted: yes
Keywords: Sequential User Modeling

Fraud Detection

Generative Pretraining

Transformer Models

Representation Learning
Abstract: Large language models (LLMs) have shown that generative pretraining can distill vast world knowledge into compact token representations. While LLMs encapsulate extensive world knowledge, they remain limited in modeling the behavioral knowledge contained within user interaction histories. User behavior forms a distinct modality, where each action—defined by multi-dimensional attributes such as time, context, and transaction type—constitutes a behavioral token. Modeling these high-cardinality, sparse, and irregular sequences is challenging, and discriminative models often falter under limited supervision. To bridge this gap, we extend generative pretraining to user behavior, learning transferable representations from unlabeled behavioral data analogous to how LLMs learn from text. We present PANTHER, a hybrid generative–discriminative framework that unifies user behavior pretraining and downstream adaptation, enabling large-scale sequential user representation learning and real-time inference. PANTHER introduces: (1) Structured Tokenization to compress multi-dimensional transaction attributes into an interpretable vocabulary; (2) Sequence Pattern Recognition Module (SPRM) for modeling periodic transaction motifs; (3) a Unified User-Profile Embedding that fuses static demographics with dynamic transaction histories, enabling both personalized predictions and population-level knowledge transfer; and (4) Real-time scalability enabled by offline caching of pre-trained embeddings for millisecond-level inference.Fully deployed and operational online at WeChat Pay, PANTHER delivers a 25.6\% boost in next-transaction prediction HitRate@1 and a 38.6\% relative improvement in fraud detection recall over baselines. Cross-domain evaluations on public benchmarks (CCT, MBD, MovieLens-1M, Yelp) show strong generalization, achieving up to 21\% HitRate@1 gains over transformer baselines, establishing PANTHER as a scalable, high-performance framework for industrial user sequential behavior modeling.

PANTHER: Generative Pretraining Beyond Language for Sequential User Behavior Modeling

Abstract

Large language models (LLMs) have shown that generative pretraining can distill vast world knowledge into compact token representations. While LLMs encapsulate extensive world knowledge, they remain limited in modeling the behavioral knowledge contained within user interaction histories. User behavior forms a distinct modality, where each action-defined by multi-dimensional attributes such as time, context, and transaction type-constitutes a behavioral token. Modeling these high-cardinality, sparse, and irregular sequences is challenging, and discriminative models often falter under limited supervision. To bridge this gap, we extend generative pretraining to user behavior, learning transferable representations from unlabeled behavioral data analogous to how LLMs learn from text. We present PANTHER, a hybrid generative-discriminative framework that unifies user behavior pretraining and downstream adaptation, enabling large-scale sequential user representation learning and real-time inference. PANTHER introduces: (1) Structured Tokenization to compress multi-dimensional transaction attributes into an interpretable vocabulary; (2) Sequence Pattern Recognition Module (SPRM) for modeling periodic transaction motifs; (3) a Unified User-Profile Embedding that fuses static demographics with dynamic transaction histories, enabling both personalized predictions and population-level knowledge transfer; and (4) Real-time scalability enabled by offline caching of pre-trained embeddings for millisecondlevel inference.Fully deployed and operational online at WeChat Pay, PANTHER delivers a 25.6% boost in next-transaction prediction HitRate@1 and a 38.6% relative improvement in fraud detection recall over baselines. Cross-domain evaluations on public benchmarks (CCT, MBD, MovieLens-1M, Yelp) show strong generalization, achieving up to 21% HitRate@1 gains over transformer baselines, establishing PANTHER as a scalable, high-performance framework for industrial user sequential behavior modeling.

1 Introduction

Online payment platforms, such as WeChat Pay, Alipay, and PayPal, process billions of transactions daily, supported by critical applications like fraud detection, credit risk assessment, and personalized marketing [1]. Modeling payment behavior at this scale is challenging: data volumes are extreme; categorical features (e.g., merchant category, payment channel) are high-cardinality; and real-time risk decisions must be delivered under 100 ms.

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Subway

Canteen

Taxi

Cinema

Popcorn

Parking

Clothes

Canteen

Coffee

Necessities

Subway

Canteen

Taxi

Daily Commute

Daily Commute

Scenic Area Visit

Mall Shopping

Historical Payment

Behavior Sequence

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Recent advances in self-supervised pretraining have transformed representation learning in language, vision, and recommendation [2-6]. Large language models (LLMs) demonstrate that generative pretraining on unlabeled text can compress extensive world knowledge into token representations. Payment platforms, however, require models that capture behavioral knowledge -the individualized regularities, intents, and deviations embedded in users' interaction histories. In this modality, each action is a structured event rather than a word; we therefore view a transaction as a behavioral token defined by multi-dimensional attributes (time, context, device, counterparty, amount). Extracting user-relevant information from these high-cardinality, sparse, and irregular sequences is essential for understanding users and for operational decision-making at scale.

Mainstream industrial approaches, adapted from recommendation systems (e.g., DeepFM [7], DCN [8], AutoFIS [9], DIEN [10]), rely primarily on supervised discriminative models for transaction-level classification. At billion-user scale they face four persistent limitations: (1) label scarcity -positive examples are too few to span the combinatorial feature space; (2) overfitting to high-dimensional categories -models memorize spurious co-occurrences rather than meaningful risk patterns; (3) latency-driven truncation of long histories-weakening the ability to leverage long-range behavior; and (4) static embeddings -biased toward frequent labels and brittle under cold-start and long-tail distributions [11].

Although sequential recommenders increasingly adopt generative objectives, they typically use generation as an end task (next-item prediction) (for example HSTU [12]) rather than as a pretraining mechanism to compress user knowledge into transferable representations. In contrast, we adopt a pretrain → adapt perspective: learn generalizable user embeddings from unlabeled behavioral logs offline , then online adapt them with lightweight discriminative heads to satisfy production latency constraints. This hybrid generative-discriminative design targets user understanding rather than only item generation, and supports multiple downstream decisions (fraud detection, transaction prediction, recommendation).

Orthogonal to this objective/framework difference, user behavior sequence data inherently differs from other sequential modalities. Unlike natural language, which is governed by grammatical structures, user behavior sequences exhibit rich recurring patterns-daily routines, weekly cycles, and seasonal trends-reflecting habitual user behaviors (illustrated in Figure 1). Standard sequential architectures, including Transformers, process events individually through self-attention mechanisms. Although powerful, these methods inadequately capture local periodicities and global relational patterns inherent to payment data, often diluting signals from lengthy sequences and missing subtle yet crucial anomalies indicative of fraud.

We propose PANTHER (Pattern AttentioN Transformer with Hybrid User ProfilER), a hybrid generative-discriminative framework that unifies user behavior pretraining with downstream adaptation for real-time inference. Offline , a PANTHER transformer is pretrained on billions of transactions to predict subsequent events, producing compact user-profile embeddings and event likelihoods that encode long-term intent and temporal dynamics. Online , a lightweight classifier fuses these cached representations with current transaction context to compute risk within milliseconds. This design leverages generative pretraining for representational power while keeping inference efficient for high-throughput systems.

To make pretraining effective on user behavior sequences and inference feasible online, PANTHER introduces three modeling components and one systems mechanism:

Token Space Compression. A structured tokenization scheme integrates contextual and counterparty attributes into unified tokens and applies frequency-aware compression to reduce dimensionality, filtering noise and stabilizing generative learning over heterogeneous inputs.

Pattern-Aware Convolutional Cross-Attention. A Sequence Pattern Recognition Module (SPRM) blends multi-scale (depthwise) convolutions with cross-attention to capture local periodicities (e.g., daily/weekly cycles) together with broader contextual dependencies-preserving cyclical signals while maintaining global relations.

User Profile Embedding with Contrastive Personalization. A dedicated user-profile token provides persistent access to user context across the sequence; a contrastive objective arranges users with similar payment behaviors nearby in latent space, improving personalization under sparsity and cold-start.

Real-Time Hybrid Inference. Pretrained user/profile embeddings are cached offline and fused online with context, recent patterns, and deviation features to produce millisecond-level posterior scores-meeting production latency constraints.

We empirically validate PANTHER on real-world WeChat Pay data, demonstrating strong generalization across fraud detection, transaction prediction, personalized user modeling, and recommendation. PANTHER yields a 25.6% improvement over Transformer baselines on internal WeChat Pay benchmarks and a 21% HR@1 gain on MovieLens-1M; on Yelp, it improves NDCG@5 by 29.6% over DCN. A production PANTHER-based fraud system at WeChat Pay improves Top-0.1% recall by 38.6% in online A/B tests, enhancing security across billions of daily transactions.

In summary, PANTHER provides a scalable, efficient approach to modeling complex sequential user behaviors by extending generative pretraining beyond language to the behavioral modality, compressing user knowledge into transferable representations, and bridging pretraining with real-time inference for industrial sequential decision-making.

2 Related Work

Sequential Deep Learning and Generative Recommendation. Deep learning methods for sequential modeling have significantly advanced recommendation and personalization, evolving from early RNN- and CNN-based models (e.g., GRU4Rec [13], Caser [14]) to recent transformer-based approaches (e.g., SASRec [15], BERT4Rec [16]). Generative sequential models, such as HSTU [12], TIGER [17], DiffuRecSys [18], and HLLM [19] have further advanced the field by modeling complex temporal dependencies and uncertainties. Despite these advancements, several aspects remain under-explored: explicit modeling of periodic behaviors (e.g., daily or weekly patterns), dedicated long-term personalized user embeddings, and computational strategies for low-latency inference. PANTHER uniquely addresses these challenges by incorporating convolutional cross-attention for periodic behavior modeling, contrastive user personalization embeddings, and cached representations enabling efficient real-time inference.

Fraud Detection in Financial Systems. Fraud detection in financial systems involves severe class imbalance, label scarcity, and real-time inference constraints. Early supervised approaches, including logistic regression, decision trees, and gradient boosting, perform effectively on structured data but face challenges with sparse, noisy, high-dimensional transaction data and extreme class imbalance [20]. Graph-based methods like R-GCNs [21] and heterogeneous GNNs [22] effectively capture relational patterns among entities, yet scalability and real-time latency in billion-user scenarios remain open research areas. PANTHER complements these methods by leveraging transformer-based generative pretraining, efficiently modeling temporal user behaviors and maintaining millisecondlevel inference.

3 PANTHER

3.1 Model Overview and Problem Formulation

PANTHER employs a two-stage architecture for payment fraud detection, combining offline generative pretraining with real-time inference. Let U denote our user base where each user u ∈ U generates a sequence of payment events X u = [ x 1 , x 2 , . . . , x L ] with x t ∈ V representing compressed transaction tokens (see §3.2). Our system aims to estimate the fraud probability:

for each new transaction x new with contextual features c new, given the user's historical sequence X u .

Stage 1: Pretraining for Next Transaction Prediction. We first learn user behavior patterns through next payment behavior prediction. Each user behavior sequence is augmented with a learnable profile token x profile encoding static attributes (see §3.4). Our transformer-based model with SPRM

User Profile

Embedding

User Profile Contrastive

Learning Loss

Conv

Cross

Attention

SPRM

Next Behavior

Prediction

Payment Behavior

Sequence

Behavior Embedding + User Profile Embedding

(food, <10)

(beverage,

<10)

(sport, 10-50)

(taxi, <10)

(grocery,

50-100)

(clinic,

50-100)

(bus, <10)

(dining,

50-100)

Learnable prototypes

Structured

Tokenization

Input

Embeddings

Updated User

Profile Emb

Backward propagation

Dilated Conv

×N layers

Hidden

states of

User Profile

User ID, Gender,

Age, City,  Payment

habit statistics, ...

Dissimilar

User Profile

Similar

User Profile

Generation

loss

Multi-Head

Self-Attention

where the loss is the negative log-likelihood of the ground-truth next behaviors over the sequence. This produces two key pretrained outputs: (1) user profile embeddings e u ∈ R d , and (2) behavior predictors X u that predict next behavior Pr θ (ˆ x L +1 | X u ) through a linear and softmax layer.

Stage 2: Hybrid Inference for Real-Time Fraud Detection. For live transactions, we compute risk scores through feature fusion:

where the function g ϕ represents a general downstream discriminative model, which can take various flexible forms depending on specific tasks or use cases, ψ ( · ) embeds transaction context features, f enc encodes the last 100 transactions via TextCNN [23], and ∆ measures the distance between predicted and observed behaviors.

This design strikes a balance between accurate long-term behavior modeling and responsive real-time decision-making, making it particularly well-suited for high-throughput fraud detection systems.

3.2 Structured Tokenization for Payment Behaviors

Unlike natural language, payment behaviors lack predefined semantic units, as each transaction is defined by a combination of contextual features (e.g., payment channel, discretized amount) and counterparty attributes (e.g., merchant category, risk level). To efficiently capture this structured, multi-dimensional information, we introduce a structured tokenization framework that integrates these attributes into a unified representation.

Each transaction token is formed as the Cartesian product of contextual and counterparty features:

where, for instance, c i might represent a payment channel such as CreditCard or RedPocket , and a j could capture the transaction amount, discretized into ranges like $10-50 and $50-100 . On the counterparty side, m k can capture the merchant category, while r l indicate the associated risk level, reflecting the merchant's historical reliability (e.g., HighRisk , LowRisk ). This tokenization approach captures the key transactional semantics by transforming raw payment behaviors into compact, domain-specific tokens (e.g., (e.g., CreditCard_$50-100_Fuel_LowRisk ).), effectively embedding transactions within a structured, context-rich feature space. This design allows the

pretraining model to learn meaningful representations of transaction behavior, preserving critical financial signals while reducing sparsity.

However, the raw combinatorial space is |V| = |C| × |A| × |M| × |R| ≈ 2M , which is prohibitively large, leading to severe sparsity and overfitting risks. To address this, we adopt a frequency-based compression , leveraging real-world transaction distributions to retain only the top K = 60 , 000 most frequent tokens, covering over 96% of historical transactions. Less frequent, long-tail combinations are mapped to a unified [UNK] token, effectively reducing the vocabulary size by 97% (from 2 M to 60k), while preserving high-impact and interpretable patterns. It maintains critical transaction semantics while significantly reducing the model's computational footprint.

3.3 Sequence Pattern Recognition Module (SPRM)

Payment sequences, unlike natural language, lack a formal grammar, yet exhibit structured recurring patterns, such as sequential routines and periodic behaviors (as illustrated in Figure 1). Accurately modeling these patterns is critical for applications like next-payment prediction and fraud detection, as they encapsulate context-aware user habits. However, standard self-attention in Transformers processes each event independently, incurring a quadratic complexity and lacking inductive biases for local and periodic structures. This inefficiency can dilute signals in long sequences and obscure subtle deviations from the routine. To address these limitations, we introduce the Sequence Pattern Recognition Module (SPRM), which incorporates two lightweight inductive biases to explicitly capture local and periodic patterns in payment sequences. Operating in parallel with the multi-head self-attention, the SPRM enhances the final representation by adding its output to the Transformer's output, forming a composite result that leverages both global context and structured pattern recognition.

(i) Local Pattern Aggregation. To capture the multi-scale nature of transactional routines, we apply depthwise dilated convolutions to the token embeddings H ∈ R T × d , using a range of kernel sizes w and dilation rates r :

Here, kernels with smaller dilation rates (e.g. w = 3 , r = 1 ) capture immediate temporal clusters, while larger dilated kernels (e.g., w = 3 , r = 2 ) capture periodic or recurring patterns, even in the presence of occasional noise or sporadic behaviors. This convolutional aggregation efficiently captures multiscale transactional patterns in linear time, providing a compact representation that encodes both short-term and long-term transactional routines while remaining robust to random fluctuations within those patterns.

(ii) Prototype-Driven Pattern Matching. To further enrich these multiscale embeddings, we introduce m learnable prototypes P ∈ R m × d , each representing a canonical spending motif (e.g., weekend leisure , weekday commute ). These prototypes act as structured anchors in the embedding space, providing a scaffold for the model to map observed behaviors to known patterns.

This cross-attention mechanism compresses each local segment onto the nearest prototypes, effectively aligning real-world sequences to interpretable, high-level motifs, while preserving local context. Unlike vanilla self-attention, which treats every token as context-free, this approach enforces a structured alignment, encouraging the model to form sparse, interpretable codes that highlight subtle departures from routine behavior.

Complexity Analysis. For a sequence of length T , standard self-attention incurs a quadratic O ( T 2 ) complexity. In contrast, SPRM introduces only a linear cost for dilated convolutions, O ( T ) , and a modest O ( Tm ) for prototype cross-attention, where m ≪ T (typically m = 64 in practice). This results in an overall complexity of approximately O ( T ) , making it feasible for long payment sequences without sacrificing long-range dependency capture.

3.4 User Profile Embedding for Personalized Payment Predictions

In PANTHER, we propose a novel user profile embedding that enables personalized transaction predictions by learning shared behavioral patterns among demographically similar users. This embedding combines static user attributes with dynamic transaction histories to form a compact latent

representation, which simultaneously serves two purposes: (1) as a personalized positional encoding that contextualizes user-specific transaction sequences, and (2) as a learnable similarity anchor that adaptively refines historical behavior patterns through contrastive learning.

Our contrastive objective follows an information-theoretic formulation:

where Pos denotes positive pairs of users sharing demographic attributes (age ± 2, same geographic region, etc.), U represents the user population, and τ controls the similarity concentration temperature. This objective maximizes mutual information between demographically similar users while maintaining separation from dissimilar counterparts through the denominator's hard negative mining over all non-positive pairs.

The complete optimization objective combines both components:

This dual-objective formulation yields three key advantages: (1) geometrically meaningful embeddings where user similarity correlates with both demographic alignment and behavioral consistency, (2) improved sample efficiency through knowledge transfer between similar users, and (3) inherent regularization that prevents overfitting to individual transaction outliers.

4 Experiments

4.1 Real-World Deployment & Performance Validation

We validate the core contributions of PANTHER through its large-scale deployment at WeChat Pay, focusing on two key aspects: pretraining efficacy (next-transaction prediction) and downstream fraud detection capabilities. This deployment addresses the challenges identified in Section 3.1, showcasing how PANTHER can enhance fraud detection and personalized user services in real-world setting.

4.1.1 Next-Transaction Prediction Benchmark

Task & Dataset: models are pretrained on 5.3B anonymized transactions (38M users over 6 months) to model user-specific behavior. The raw transactions data, consisting six attributes (amount, merchant category, etc.), are tokenized by 60k interpretable tokens with the structured tokenization scheme.

Experimental Setup: In the next-transaction prediction task, we evaluate PANTHER's ability to predict the next transaction based on a user's historical data. The model leverages the SPRM and unified user-profile embeddings for this purpose. We employ two widely-adopted evaluation metrics: HR@K(Hit Ratio at K) and NDCG@K (Normalized Discounted Cumulative Gain at K). Specifically, HR@K measures the fraction of test instances in which the ground-truth item appears among the top-K predicted items. NDCG@K assesses the ranking quality by assigning higher weights to relevant items placed at top positions, normalized by the ideal discounted gain. The code is available at https://github.com/WeChatPay-Pretraining/PANTHER .

1 Local Services

2 Travel Platforms

3 Ride Platforms

1 Travel Platforms

2 Public Transport

3 E-commerce

1 Recharge Stations

2 Gas Stations

3 Online Media

Pattern Basis

Travel & Tourism Spending

Quick Meals & Convenience Shopping

Leisure & Social Spending

1 Desserts & Drinks

2 Retail Industries

3 Fast Food

1 Snacks

2 Baked Goods

3 C-Store

1 Baked Goods

2 C-Store

3 C-Store

1 Games

2 Desserts &  Drinks

3 Games

1 Spa & Health

2 Bars

3 Games

1 Cafes

2 Ride Platforms

3 Games

Cross Attention Scores Heatmap

0.3

0.2

0.1

We benchmark PANTHER against several strong baseline models, including Transformer [24], SASRec [15], and HSTU [12].

Key Findings : As shown in Table 1, PANTHER outperforms baseline models significantly. The SPRM module alone improves HR@1 by 14.9%, highlighting the value of sequential behavior patterns. Incorporating user profiles via learnable positional encoding boosts HR@1 by an additional 5.5%. Adding contrastive learning objectives results in a total HR@1 improvement of 25.6%, demonstrating the effectiveness of context-aware embeddings and knowledge transfer.

Visualization of Learned Patterns: Figure 3 shows how PANTHER maps consecutive user behaviors to learned behavior prototypes (P1, P2, P3). Each column represents a sequence of three consecutive behaviors identified as recurring patterns in the user's history. The heatmap values indicate the strength of alignment between behaviors and prototypes, with higher values signifying stronger matches. This visualization highlights how the model captures and recognizes repeating transaction motifs over time.

4.1.2 Hybrid Real-Time Fraud Detection

›We evaluate PANTHER's fraud detection performance through a 10-day full-traffic A/B test on WeChat Pay's production system, using the DeepFM model [7] as the baseline. DeepFM relies on handcrafted features and a TextCNN encoder for sequence processing. PANTHER operates in a hybrid configuration, combining offline-pretrained user-profile embeddings ( e u ) and behavioral anomaly scores ( ∆ ) with real-time transaction features (Equation 3), achieving substantial recall improvements, as shown in Figure 4.

The hybrid PANTHER model improves fraud recall by 109.5% at the 0.01% threshold, with smaller gains at higher thresholds (0.1% +38.6% , 1% +12.1%). The larger gains at extreme thresholds highlight the model's effectiveness in detecting low-frequency, high-risk fraud cases using personalized embeddings ( e u ) and behavioral anomaly scores ( ∆ ).

Deployment Strategy: This hybrid approach introduces minimal overhead (5ms higher than the baseline), while offering three key advantages: (1) personalized fraud detection via user-specific embeddings, (2) explainable anomaly detection through interpretable deviation scores, and (3) scalable production deployment by separating compute-intensive pretraining from real-time inference.

4.1.3 Merchant Risk Assessment via Behavior Sequence Pretraining

In addition to fraud detection, we developed a framework for merchant risk assessment based on behavior sequence pretraining. This approach identifies merchants potentially involved in fraud by analyzing deviations in user behavior at the merchant level, incorporating components for behavior prediction, deviation measurement, risk aggregation, and merchant classification.

Next Behavior Deviation Scoring. Deviation is measured as the standardized difference between the predicted likelihood of a behavior and the user's typical behavior distribution. It reduces false positives for users with naturally varied transaction patterns: ∆ u,m = P u ( m ) -µ u σ u , where µ u and σ u are the mean and Std Dev of predicted probabilities across all potential behaviors for user u .

DeepFM (Baseline)

+ User Profile Embedding + Behavioral Anomaly Score

+ Both Components

Model Configuration

Improvement (Relative to Baseline)

0.0%

61.6%

64.2%

109.5%

0.0%

16.8%

21.1%

38.6%

0.0%

9.3%

4.1%

12.1%

Model Improvement by Configuration and Top Percentage

Top 0.01%

Top 0.1%

Top 1%

Merchant Risk Scoring and Classification. We aggregate deviation scores for all users transacting with merchant m and compute the quantile-based risk score:

where U m is the set of users interacting with merchant m . Robust statistical features are extracted and used as inputs for classifier (e.g., XGBoost) to label merchants as high or low risk.

Practical Impact. The framework showed strong performance in practice, achieving 76% accuracy for flagged high-risk merchants - a significant improvement over the baseline method's 50% accuracy.

4.2 Benchmark Performance

After validating PANTHER's real-world viability, we evaluate its performance on public benchmark datasets to quantify its improvements over existing methods. This section compares PANTHER against strong baselines on public transaction datasets and recommendation benchmark, demonstrating consistent performance gains across domains beyond the WeChat Pay setting.

4.2.1 Datasets & Tasks

We evaluate PANTHER's pretraining performance on four datasets, including two financial datasets and two recommendation datasets, to assess its effectiveness and generalizability. For downstream tasks, we compare fraud detection performance on CCT and recommendation tasks on Yelp (since only these datasets include user profile features).

Credit Card Transactions (CCT) [25]: A synthetic dataset with 20 million transactions from 2,000 users, used for fraud detection with embedded anomalies.

MBD-mini [26]: An anonymized banking dataset tracking monthly product purchases, focusing on repetitive consumer behaviors.

MovieLens-1M [27]: A widely-used recommendation dataset with 1 million user-item interactions, ideal for evaluating sequential models in non-financial domains.

Yelp [28]: A public recommendation dataset containing millions of user-business interactions (e.g., restaurant reviews), commonly used for sequential recommendation tasks.

Each dataset is split chronologically into training, validation, and test subsets.

Implementation details: We apply the same model hyperparameters to the open benchmark datasets as those used for the WeChat Pay dataset. Full implementation details are provided in Section B and our supplementary code.

4.2.2 Next Payment Behavior Prediction Benchmarking

We assess PANTHER on next behavior prediction. As summarized in Table 2 for MBD-mini, CCT, MovieLens-1M and Yelp, it consistently outperform strong baselines like SASRec and HSTU.

* The higher HR@10 reported SASRec [15] is due to sampled negative evaluation ( ∼ 100 items). Our protocol follows HSTU with full-ranking over ∼ 3,700 items.

Key Observations: On WeChat Pay, PANTHER-large achieves a 25.56% HR@1 improvement over Transformer, reflecting its capability to model sporadic, large-scale financial transactions. On MBD-mini and CCT, PANTHER improves HR@1 by 2.95% and 15.2%, respectively, demonstrating its broad applicability to other payment transaction data. On the MovieLens-1M and Yelp datasets, PANTHER improves HR@1 by 21.8% and 9.17% over Transformer, surpassing HSTU and showing strong generalization beyond payment data. Overall, PANTHER demonstrates robustness across domains and highlights the advantages of larger model configurations for complex user-item interactions.

4.2.3 Hybrid Fraud Detection & Recommendation

We evaluate the PANTHER on downstream fraud detection and recommendation tasks, by introducing its pretrained embeddings ( e u ) and next-behavior predictions ( ˆ X t ) to the baseline models.

Hybrid Recommendation on Yelp. We demonstrate that PANTHER not only excels in fraud detection but also performs effectively in recommendation tasks. For example, on the Yelp dataset, the NDCG@5 metric improves by 29.6% over the DCN baseline (Table 3), highlighting the value of pretrained embeddings in enhancing recommendation performance.

Hybrid Fraud Detection on CCT. On the CCT dataset, incorporating PANTHER's pretrained embeddings and predictions improves fraud detection recall by 19.9% over the TabBERT-MLP ([29]) baseline (Table 4), enhancing the model's effectiveness in detecting fraudulent activity.

These results confirm that PANTHER's hybrid method significantly boosts performance across tasks by leveraging learned user profiles and behavior predictions.

4.3 Transferability

We examine PANTHER's ability to transfer learned representations across new users, datasets, and domains, demonstrating the power of generative pretraining in low-label and cross-domain scenarios. PANTHER shows exceptional transferability, with a 301.4% improvement over cold-start baselines when transferring across user demographics. Additionally, fine-tuning on external datasets after pretraining on WeChat Pay leads to an average HR@1 improvement of 16.66% on MBD-mini and CCT, showcasing the model's adaptability across diverse transaction contexts. More detailed results and experiments are provided Section C. These findings highlight PANTHER's ability to generalize effectively with minimal retraining, making it highly suitable for real-world applications with sparse labeled data.

4.4 Summary of Experimental Findings

Our experiments show that PANTHER consistently outperforms strong baselines across tasks. It achieves robust next-transaction prediction with noisy, sparse data by leveraging the Sequence Pattern Recognition Module and adaptive user embeddings. PANTHER also demonstrates strong transferability, excelling across diverse domains, including transaction data (CCT, MBD-mini) and recommendation tasks (MovieLens-1M, Yelp). Real-world deployment at WeChat Pay shows a 38% improvement in fraud recall at top 0.1%. Ablation studies confirm that key components, like SPRM and contrastive learning, significantly enhance performance. Overall, it proves to be a versatile, scalable solution for sequential behavior modeling, with strong generalization across domains.

5 Conclusions

We introduced PANTHER, a generative pretraining framework that addresses real-world payment data complexities by combining noise suppression, pattern recognition, and user personalization. Through token space compression and innovative attention mechanisms, PANTHER uncovers subtle, cyclic behaviors and incorporates long-term user context, producing high-fidelity user embeddings. This design supports critical financial applications such as fraud detection, credit scoring, nextpayment prediction, and user segmentation. Beyond industry use, PANTHER highlights an important direction for the machine learning community: leveraging massive unlabeled transaction logs to enhance efficiency and adaptability. As financial and e-commerce data volumes rise, PANTHER's integration of generative modeling and personalization enables more secure, accurate, and user-centric services. However, its lack of interpretability is a limitation, as complex representations may hinder transparency in high-stakes applications. Future work will focus on improving explainability for broader applicability in regulated domains.

Acknowledgments

This work is supported by the National Natural Science Foundation of China (No. 62406192), Opening Project of the State Key Laboratory of General Artificial Intelligence (No. SKLAGI2024OP12), and Tencent WeChat Rhino-Bird Focused Research Program.

References

Enterprise Apps Today. Global digital payment platforms user statistics (2022). https: //www.enterpriseappstoday.com/stats/online-payment-statistics.html , 2022. Accessed: 2025-01-23.

Jacob Devlin. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 , 2018.

Yu Sun, Shuohuan Wang, Yukun Li, Shikun Feng, Hao Tian, Hua Wu, and Haifeng Wang. Ernie 2.0: A continual pre-training framework for language understanding. In Proceedings of the AAAI conference on artificial intelligence , volume 34, pages 8968-8975, 2020.

Mark Chen, Alec Radford, Rewon Child, Jeffrey Wu, Heewoo Jun, David Luan, and Ilya Sutskever. Generative pretraining from pixels. In International conference on machine learning , pages 1691-1703. PMLR, 2020.

Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick. Masked autoencoders are scalable vision learners. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages 16000-16009, 2022.

Jiaqi Zhai, Lucy Liao, Xing Liu, Yueming Wang, Rui Li, Xuan Cao, Leon Gao, Zhaojie Gong, Fangda Gu, Michael He, et al. Actions speak louder than words: Trillion-parameter sequential transducers for generative recommendations. arXiv preprint arXiv:2402.17152 , 2024.

Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, and Xiuqiang He. Deepfm: A factorization-machine based neural network for ctr prediction. Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI) , pages 1725-1731, 2017.

Ruoxi Wang, Bin Fu, Gang Fu, and Mingliang Wang. Deep & cross network for ad click predictions. In Proceedings of the ADKDD'17 , pages 1-7. 2017.

Bin Liu, Chenxu Zhu, Guilin Li, Weinan Zhang, Jincai Lai, Ruiming Tang, Xiuqiang He, and Zhenguo Li. Autofis: Automatic feature interaction selection in factorization models for click-through rate prediction. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining , pages 2636-2645, 2020.

Guorui Zhou, Na Mou, Ying Fan, Qi Pi, Weijie Cai, Yang Liu, Han Zhu, Xiao Ma, Yanghui Yan, Junqi Jin, and Kun Gai. Deep interest evolution network for click-through rate prediction. Proceedings of the AAAI Conference on Artificial Intelligence , 33(1):5941-5948, 2019.

Paul Covington, Jay Adams, and Emre Sargin. Deep neural networks for youtube recommendations. Proceedings of the 10th ACM Conference on Recommender Systems (RecSys) , pages 191-198, 2016.

Jiaqi Zhai, Lucy Liao, Xing Liu, Yueming Wang, Rui Li, Xuan Cao, Leon Gao, Zhaojie Gong, Fangda Gu, Michael He, et al. Actions speak louder than words: Trillion-parameter sequential transducers for generative recommendations. arXiv preprint arXiv:2402.17152 , 2024.

Balazs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. Session-based recommendations with recurrent neural networks. In Proceedings of the 4th International Conference on Learning Representations (ICLR) , 2016. URL https://arxiv.org/abs/ 1511.06939 .

Jiaxi Tang and Ke Wang. Personalized top-n sequential recommendation via convolutional sequence embedding. In Proceedings of the 11th ACM International Conference on Web Search and Data Mining (WSDM) , pages 567-575, 2018. doi: 10.1145/3159652.3159656.

Wang-Cheng Kang and Julian McAuley. Self-attentive sequential recommendation. In IEEE International Conference on Data Mining (ICDM) , pages 1236-1242, 2018. doi: 10.1109/ ICDM.2018.00152.

Fei Sun, Jun Liu, and Jian Wu. Bert4rec: Sequential recommendation with bidirectional encoder representations from transformers. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management (CIKM) , pages 1441-1450, 2019. doi: 10.1145/3357384.3357895.

Shubham Rajput, Mingxuan Chen, and Fei Sun. Tiger: Token-based generative retrieval for sequential recommendation. In Advances in Neural Information Processing Systems (NeurIPS) , pages 789-798, 2023. doi: 10.48550/arXiv.2306.01234.

Majid Zolghadr, Mohsen Jamali, and Jiawei Zhang. Diffurecsys: Diffusion-based generative modeling for sequential recommendation. Proceedings of the ACM Web Conference (WWW) , pages 2156-2165, 2024. doi: 10.1145/3545678.3557899.

Junyi Chen, Lu Chi, Bingyue Peng, and Zehuan Yuan. Hllm: Enhancing sequential recommendations via hierarchical large language models for item and user modeling. arXiv preprint arXiv:2409.12740 , 2024.

Richard J Bolton and David J Hand. Statistical fraud detection: A review. Statistical Science , 17(3):235-255, 2002. doi: 10.1214/ss/1042727940.

Humberto Acevedo-Viloria, Juan Martinez, and Maria Garcia. Relational graph convolutional networks for financial fraud detection. IEEE Transactions on Knowledge and Data Engineering , 33(7):1357-1370, 2021. doi: 10.1109/TKDE.2020.3007655.

Hao Wang, Wei Zhang, and Fei Sun. Heterogeneous graph neural networks for user behavior prediction. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management (CIKM) , pages 765-774, 2019. doi: 10.1145/3357384.3357907.

Yoon Kim. Convolutional neural networks for sentence classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) , pages 1746-1751, 2014.

A Vaswani. Attention is all you need. Advances in Neural Information Processing Systems , 2017.

Eric Altman et al. Credit card transactions (cct) dataset, 2019. URL https://www.kaggle. com/datasets/ealtman2019/credit-card-transactions . Accessed: 2025-01-31.

AI Lab et al. Mbd-mini dataset, 2020. URL https://huggingface.co/datasets/ai-lab/ MBD-mini . Accessed: 2025-01-31.

GroupLens et al. Movielens-1m dataset, 2000. URL https://grouplens.org/datasets/ movielens/1m/ . Accessed: 2025-01-31.

Yelp. Yelp dataset, 2014. URL https://business.yelp.com/data/resources/ open-dataset/ .

Inkit Padhi, Yair Schiff, Igor Melnyk, Mattia Rigotti, Youssef Mroueh, Pierre Dognin, Jerret Ross, Ravi Nair, and Erik Altman. Tabular transformers for modeling multivariate time series. In ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , pages 3565-3569. IEEE, 2021.

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A Notation Table

We summarize the frequently used notations in Table 5.

Appendix

B Implementation Details

In this section, we provide the full implementation details for MBD-mini, CCT, Movielens, and Yelp datasets, including model configurations, training procedures, and tokenization methods.

B.1 CCT, Yelp, and MBD-mini Experiments

For the CCT, Yelp, and MBD-mini dataset, we configured PANTHER with 4 layers and 2 attention heads . Training was conducted with a batch size of 128 at learning rate 1 × 10 -3 . The training utilized a single GPU over a span of 2 hours on CCT, 6 hours on Yelp, and 12 hours hours on MBD-mini.

The Transformer, SASRec, and HSTU models were configured with the same learning rate, number of layers, and batch size as PANTHER.

B.2 MovieLens-1M Experiments

For the MovieLens-1M experiments, PANTHER was trained with a batch size of 128 and a learning rate of 1 × 10 -3 . Specifically, PANTHER was built with a 2-layer , 1-head configuration. All baseline models were configured identically to PANTHER.

These experiments followed the same training pipeline as WeChatPay, with hierarchical tokenization and discretization applied to transaction attributes. Given the smaller dataset sizes, training was completed within a shorter time frame while preserving model scalability.

B.3 Tokenization of benchmark datasets

For the CCT dataset, user behavior tokens are constructed from payment amounts, payment methods, and merchant categories, resulting in a vocabulary of 16,847 tokens. User profiles include available card and card-holder information such as card brand, card type, and user age. For the MBD-mini dataset, user behavior tokens are derived from transaction attributes including amount, currency, event type, and the source and destination types, yielding a vocabulary of 40,791 distinct tokens.

In the Yelp dataset, user behavior tokens are formed by combining a business's city, category, star rating, and review count, where continuous features are bucketized. The original vocabulary of 40K tokens can be compressed to 17K tokens, covering 95% of all user-business interactions. User profiles consist of attributes such as the number of friends, number of reviews, and average star ratings. For the Movielens dataset, movie IDs are directly used as behavior tokens.

B.4 Efficiency Evaluation

We compare GPU memory usage and inference time of the SPRM against the Transformer baseline across increasing sequence lengths, in Table 6. These experiments illustrate how SPRM scales more efficiently in both memory consumption and latency, particularly when handling longer sequences where the Transformer model fails due to memory overflow.

C Transferability of PANTHER

We examine PANTHER 's capacity to transfer learned representations to new users, new datasets, and new domains. The experiments showcase the advantage of generative pretraining in label scarcity and cross-domain scenarios.

C.1 User-Level Transferability

We pre-train PANTHER on one set of WeChatPay users and evaluate on another disjoint set for a cold-start recommendation setting. As shown in Table 7, the pre-trained PANTHER significantly outperforms the cold-start baseline, demonstrating its ability to preserve learned behavioral patterns and adapt to new users with minimal retraining. This finding is crucial for financial applications where new users frequently arrive and labeled data are sparse.

C.2 Data-Level Transferability

To assess cross-dataset adaptability, we pre-train PANTHER on WeChatPay and fine-tune it on MBD-mini and CCT. Table 8 shows an average HR@1 improvement of 16.66%, demonstrating effective transfer of our generative pretraining across diverse transaction contexts. The model retains valuable cross-data signals, such as user spending patterns, even when the merchant or product space changes.

D Ablation Experiments

To examine the sensitivity of PANTHER to the balance between L gen and L CL, we vary the loss coefficient λ and report the corresponding performance. The results are summarized in Table 9.

Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents a framework for fraud detection in payment platforms, seemingly inspired by self-attentive sequential recommendation systems [17]. The proposed approach consists of an unsupervised pre-training step to derive long term user profiles in the form of user embeddings, followed by a supervised step that can be trained using a limited amount of labelled examples.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\nS1: Evaluation of several different tasks and datasets including \n- WeChat data for next transaction prediction and merchant risk assessment \n- a publicly available Credit Card Transaction dataset with labels of fraudulent transactions \n- Movielens and Yelp datasets for recommendations on sequential data.\n\nWeaknesses: \n\nW1: The paper is not clearly written, the notation and the terms used are not properly introduced: \n- HR and NDCG are not defined\n- T is not introduced\n- \\psi is introduced, but how it is trained/computed is not described.\n\nW2: P is used in many different meanings, which makes the paper very difficult to parse: \n- In Equations (1), (2) and (3), P means probability\n- In Section 3.3.2, P appears to represent a learnable prototype of dimension d\n- In Section 3.4, P denotes positive pairs of users sharing demographic attributes\n\nW3: The proposed solution is compared with the same two baselines (SASRec [17] and HSTU [19]) independent of the task & dataset:   \n- SASRec [17] is from 2018.  This reviewer was not able to locate HSTU [19] in the proceedings of ICML 2024.\n- The Hit Rate @ 10 reported in [17] for Movielens is 0.8245, significantly higher than the value reported in this paper, which is 0.2853.\n- It is very unlikely that these two baselines (one of which cannot be located) could produce state-of-the-art results for all the tasks and all the datasets evaluated. The authors should have compared their solution with the methods that produce state-of-the-art results.\n\nQUESTIONS:\nQ1: Why do the authors use only SASRec [17] and HSTU [19] in all their comparisons? \n\nQ2: Could the authors provide a link to HSTU [19]?\n\nQ3: Why aren't the SASRec results provided by the authors consistent with the results given in [17] for the Movielens 1M dataset?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces PAINTER, a hybrid framework that enables real-time, large-scale fraud detection in payment systems. PAINTER leverages generative pretraining on unlabeled transaction data and user profile embedding to capture personalized user behavior. Experiments show significant improvements in fraud detection, next-transaction prediction, and other recommendation tasks, outperforming existing methods.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths:**\n1. The paper tackles an important and timely issue: efficient and effective fraud detection in real-time, billion-scale scenarios, which is crucial for large-scale payment systems that require both speed and accuracy.\n2. The pretraining module for next transaction prediction, which fuses high-dimensional data alongside local and global relational pattern recognition, presents an interesting approach for characterizing user behavior.\n3. The comprehensive evaluation across multiple tasks and datasets demonstrates the robustness and generalization of the proposed framework. The model performs well on the real-world datasets and dataset from WeChat Pay, achieving notable improvements in different prediction tasks.\n\n**Weaknesses:**\n1. While the authors emphasize the efficiency of their approach, there is no specific experimental validation in this paper. Incorporating an experiment that measures how training time or computational resources for both the proposed model and baseline methods scale with increasing data volume would strengthen this claim.\n2. Several existing approaches (e.g., Perceiver) adopt prototype-inspired techniques to reduce transformer complexity to $\\mathcal{O}(mT)$. It would be better if the authors clarified how their Pattern Matching Transformer outperforms these models in the context of user behavior modeling.\n3. The structured tokenization scheme is relatively simple and relies on a frequency-based vocabulary. This approach risks mapping low‐frequency but critical fraud indicators to an UNK token, thereby weakening the model’s ability to detect novel or rare fraud patterns. Moreover, once deployed, the fixed vocabulary cannot be updated dynamically; in environments where transaction distributions shift over time, the tokenization strategy needs timely updating, and the model requires retraining.\n\n[1] Perceiver: General Perception with Iterative Attention\n\nQUESTIONS:\nSee weaknesses. I list some other questions here.\n\n1. Could the authors provide some statistical summaries of the WeChat Pay dataset to illustrate its scale and characteristics?\n2. How are these textual payment behaviors transformed into token embeddings? Does this process utilize a pretrained BERT or large language model, and is this module updated during model training?\n3. Given that the frequent-token set will challenge as transaction distribution evolve over time, is it necessary to retrain the model in such scenarios?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposes a large sequential behavioral modeling model called PANTHER. The modeling approach proceeds in two steps. In the first step, the model is trained to predict the next transaction with a self-supervised learning objective. The model is trained to predict the next transaction based on structured tokens which take into account merchant and transaction information and risk profiles. The model also predicts user embeddings which are learned with contrastive learning with hard negative sampling. The user embedding and the predicted next transaction are used during inference, along with embeddings of the last 100 transactions, to predict whether the next transaction is fraudulent, using a light weight model. The resulting model is evaluated on real world data from WeChat pay against state-of-the-art baselines including Transformer. The model is also evaluated on some public datasets, and demonstrates promising performance.\n\nSTRENGTHS AND WEAKNESSES:\n## Strengths\nThe paper is well written, the problem is well motivated, and the method is novel to the best of knowledge. The resulting model achieves promising performance. The model is evaluated on a large real world dataset, and against state-of-the-art baselines. The code is also publicly available (in the supplementary material). \n\n## Weaknesses\n[Major] I am not sure if the paper is **double blind** given that it is evaluated on datasets collected by WeChat Pay. It is reasonable for me to assume that such a dataset can only be provisioned by Tenecent. That said, I do not think this has clouded by judgement of the paper. \n\n1. Please expand and define HR (Hit Rate) and NDCG on first use, and define them in the paper. \n2. There are a bunch of moving parts / components in the paper. I would encourage the authors to run / present ablation experiments for their design choices. For example, what is the impact of personalized user embeddings? What is the impact of using a transformer instead of the CNN architecture? What is the impact of the prototypes? \n3. Scaling laws: I wonder what is the impact of pre-training PANTHER on multiple transactional datasets? What is the impact of scaling the data (# of transactions and users) and the model size? \n\n## Minor Issues\n1. Typo in line 68\n\nQUESTIONS:\n1. Could you explain how your model will adapt to a new user that it is not trained on? How are the user embeddings generated?\n2. Could you please provide the summary statistics of this dataset? What are the fields? How many users is it from? What is the time range, # of transactions, average length of transaction, etc. What are the different features in this dataset? Will a subset of this dataset be made public? How is the fraudulent data labeled? Can there me false negatives (i.e. fraudulent transactions labeled as normal). How is the train and test split is chosen?\n3. What is the context length of the model? How many tokens can it take at a time?\n\nLIMITATIONS:\nThe authors have briefly discussed the limitations, but I would encourage the authors to discuss have a dedicated section on limitations.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 4, "ORIGINALITY": 3, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper introduces PANTHER, a hybrid framework combining self-supervised generative pretraining and lightweight discriminative modeling for fraud detection in large-scale payment systems. Key innovations include structured tokenization, a sequence pattern recognition module (SPRM), and a contrastively-learned user profile embedding. The model demonstrates strong real-world performance (e.g., deployed at WeChat Pay) and generalizes well to public benchmarks.\n\nSTRENGTHS AND WEAKNESSES:\n1. The method presents several targeted architectural innovations. Structured tokenization converts multi-field transaction data into meaningful and compact semantic units, addressing the challenge of high-cardinality categorical features. The SPRM module enhances behavioral pattern modeling by combining dilated convolutions with prototype-based attention. Furthermore, the use of contrastive user embeddings integrates both static profiles and dynamic histories, significantly improving performance in cold-start scenarios.\n\n2. The proposed approach is supported by extensive experimental evidence. It shows significant improvements in real-world A/B testing, generalizes well to other domains in benchmark comparisons, and includes detailed ablation studies that clarify the contribution of each component. \n\n3. The framework has been deployed in a large-scale payment platform, where its combination of offline generative pretraining and efficient online inference achieves a good trade-off between detection accuracy and latency.\n\n4. The paper introduces the function $g_\\phi(\\cdot)$ in Equation (3) as part of the hybrid inference model, but its exact form or implementation is not explained. \n\n5.  On page 4, the authors state that token compression reduces the vocabulary size from approximately 2 million to 60,000, claiming a 99.7% reduction. However, the correct reduction is 97% (i.e., (1 - 60,000 / 2,000,000) × 100%). This appears to be a calculation error and should be corrected.\n\nQUESTIONS:\n1. The two baseline models used in the experiments, DeepFM and DCN, are relatively outdated. Why did the authors choose these models instead of more recent baselines ? While I understand that lightweight discriminative models like DeepFM and DCN are favorable for fast inference, including comparisons with more recent and stronger baselines would help more comprehensively demonstrate the essential role and contribution of PANTHER.\n2. On page 6, the authors formulate the complete optimization objective as a balance between individual fidelity and population structure, controlled by the hyperparameter \\lambda. However, the paper does not include the experimental analysis on how different values of \\lambda affect model performance. Such an ablation study would be valuable to understand the sensitivity of the model to this trade-off and to justify the chosen value.\n3. Tokenization for public datasets (Appendix A) lacks specifics: How are continuous features (e.g., Yelp’s \"review count\") bucketized ? Why does MovieLens dataset directly use raw movie IDs (not structured tokens) ?\n\nLIMITATIONS:\nSee the Weaknesses and Questions parts.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-06"}