Beyond One-Size-Fits-All: Adaptive Test-Time Augmentation for Sequential Recommendation

• URL: http://arxiv.org/abs/2604.16121v1
• Date: Fri, 17 Apr 2026 14:56:29 GMT
• Title: Beyond One-Size-Fits-All: Adaptive Test-Time Augmentation for Sequential Recommendation
• Abstract summary: Test-time augmentation has become a promising approach for mitigating data sparsity in sequential recommendation.<n>Existing TTA methods typically rely on uniform, user-agnostic augmentation strategies.<n>AdaTTA learns to select sequence-specific augmentation operators on a per-sequence basis.
• Score: 3.873910497341511
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Test-time augmentation (TTA) has become a promising approach for mitigating data sparsity in sequential recommendation by improving inference accuracy without requiring costly model retraining. However, existing TTA methods typically rely on uniform, user-agnostic augmentation strategies. We show that this "one-size-fits-all" design is inherently suboptimal, as it neglects substantial behavioral heterogeneity across users, and empirically demonstrate that the optimal augmentation operators vary significantly across user sequences with different characteristics for the first time. To address this limitation, we propose AdaTTA, a plug-and-play reinforcement learning-based adaptive inference framework that learns to select sequence-specific augmentation operators on a per-sequence basis. We formulate augmentation selection as a Markov Decision Process and introduce an Actor-Critic policy network with hybrid state representations and a joint macro-rank reward design to dynamically determine the optimal operator for each input user sequence. Extensive experiments on four real-world datasets and two recommendation backbones demonstrate that AdaTTA consistently outperforms the best fixed-strategy baselines, achieving up to 26.31% relative improvement on the Home dataset while incurring only moderate computational overhead

Related papers

• Think2Go: Generative Next POI Recommendation with LLM Reasoning [57.37706163990998]
  Next Point-of-Interest (POI) recommendation task focuses on mining user behavioral preference patterns from historical check-ins to provide personalized suggestions for the next destination.<n>Existing methods rely on shallow contextual information and handcrafted feature interactions to predict the next POI.<n>We propose Think2Go, a novel generative next POI recommendation framework, which enhances the model's comprehension of SID representations and explores diverse spatial-temporal patterns via test-time computational scaling.
  arXiv  Detail & Related papers  (2026-07-31T03:52:20Z)
• FedUTR: Federated Recommendation with Augmented Universal Textual Representation for Sparse Interaction Scenarios [58.33672784127872]
  Federated recommendations (FRs) have emerged as an on-device privacy-preserving paradigm.<n>We propose a novel method named FedUTR, which incorporates item textual representations as a complement to interaction behaviors.<n>Our method achieves superior performance, with improvements of up to 59% across all datasets compared to the SOTA baselines.
  arXiv  Detail & Related papers  (2026-01-29T08:55:19Z)
• GDPO: Group reward-Decoupled Normalization Policy Optimization for Multi-reward RL Optimization [133.27496265096445]
  We show how to apply Group Relative Policy Optimization under multi-reward setting without examining its suitability.<n>We then introduce Group reward-Decoupled Normalization Policy Optimization (GDPO), a new policy optimization method to resolve these issues.<n>GDPO consistently outperforms GRPO, demonstrating its effectiveness and generalizability for multi-reward reinforcement learning optimization.
  arXiv  Detail & Related papers  (2026-01-08T18:59:24Z)
• AsarRec: Adaptive Sequential Augmentation for Robust Self-supervised Sequential Recommendation [18.47848960425837]
  Sequential recommender systems have demonstrated strong capabilities in modeling users' dynamic preferences and capturing item transition patterns.<n>Human errors, uncertainty, and behavioral ambiguity can lead to degraded recommendation performance.<n>We introduce AsarRec, which learns to generate transformation matrices by encoding user sequences into probabilistic transition sequences.
  arXiv  Detail & Related papers  (2025-12-16T03:29:11Z)
• Listwise Preference Diffusion Optimization for User Behavior Trajectories Prediction [41.53271688465831]
  We formulate User Behavior Trajectory Prediction (UBTP) as a new task setting that explicitly models long-term user preferences.<n>We introduce Listwise Preference Diffusion Optimization (LPDO), a diffusion-based training framework that directly optimize structured preferences over entire item sequences.<n>To rigorously evaluate multi-step prediction quality, we propose the task-specific metric Sequential Match (SeqMatch), which measures exact trajectory agreement, and adopt Perplexity (PPL), which assesses probabilistic fidelity.
  arXiv  Detail & Related papers  (2025-11-01T12:16:24Z)
• Adaptive Preference Optimization with Uncertainty-aware Utility Anchor [33.74005997646761]
  offline preference optimization methods are efficient for large language models (LLMs) alignment.<n>We propose a general framework for offline preference optimization methods, which introduces an anchoring function to estimate the uncertainties brought from preference data annotation.<n>Our method enables training even in scenarios where the data is unpaired, significantly enhancing data utilization efficiency.
  arXiv  Detail & Related papers  (2025-09-03T10:20:08Z)
• LARES: Latent Reasoning for Sequential Recommendation [96.26996622771593]
  We present LARES, a novel and scalable LAtent REasoning framework for Sequential recommendation.<n>Our proposed approach employs a recurrent architecture that allows flexible expansion of reasoning depth without increasing parameter complexity.<n>Our framework exhibits seamless compatibility with existing advanced models, further improving their recommendation performance.
  arXiv  Detail & Related papers  (2025-05-22T16:22:54Z)
• On the Role of Feedback in Test-Time Scaling of Agentic AI Workflows [71.92083784393418]
  Agentic AI (systems that autonomously plan and act) are becoming widespread, yet their task success rate on complex tasks remains low.<n>Inference-time alignment relies on three components: sampling, evaluation, and feedback.<n>We introduce Iterative Agent Decoding (IAD), a procedure that repeatedly inserts feedback extracted from different forms of critiques.
  arXiv  Detail & Related papers  (2025-04-02T17:40:47Z)
• Adaptive Preference Scaling for Reinforcement Learning with Human Feedback [103.36048042664768]
  Reinforcement learning from human feedback (RLHF) is a prevalent approach to align AI systems with human values. We propose a novel adaptive preference loss, underpinned by distributionally robust optimization (DRO) Our method is versatile and can be readily adapted to various preference optimization frameworks.
  arXiv  Detail & Related papers  (2024-06-04T20:33:22Z)
• Direct Preference Optimization With Unobserved Preference Heterogeneity: The Necessity of Ternary Preferences [14.686788596611246]
  Reinforcement Learning from Human Feedback (RLHF) has become central to aligning large language models with human values.<n>Recent alternatives such as Direct Preference Optimization (DPO) simplify this pipeline by directly optimizing on preferences.<n>We propose a theoretical and algorithmic framework for fairness and personalization for diverse users in generative model alignment.
  arXiv  Detail & Related papers  (2024-05-23T21:25:20Z)
• Bidirectional Looking with A Novel Double Exponential Moving Average to Adaptive and Non-adaptive Momentum Optimizers [109.52244418498974]
  We propose a novel textscAdmeta (textbfADouble exponential textbfMov averagtextbfE textbfAdaptive and non-adaptive momentum) framework. We provide two implementations, textscAdmetaR and textscAdmetaS, the former based on RAdam and the latter based on SGDM.
  arXiv  Detail & Related papers  (2023-07-02T18:16:06Z)
• Sequence Adaptation via Reinforcement Learning in Recommender Systems [8.909115457491522]
  We propose the SAR model, which learns the sequential patterns and adjusts the sequence length of user-item interactions in a personalized manner. In addition, we optimize a joint loss function to align the accuracy of the sequential recommendations with the expected cumulative rewards of the critic network. Our experimental evaluation on four real-world datasets demonstrates the superiority of our proposed model over several baseline approaches.
  arXiv  Detail & Related papers  (2021-07-31T13:56:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.