Copertina

Data Augmentation as Free Lunch: Exploring the Test-Time Augmentation for Sequential Recommendation

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Dang, Yizhou, Liu, Yuting, Yang, Enneng, Huang, Minhan, Guo, Guibing, Zhao, Jianzhe, Wang, Xingwei
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Information Retrieval
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Data Augmentation for Sequential Recommendation: A Survey
di: Dang, Yizhou, et al.
Pubblicazione: (2024)

Repeated Padding+: Simple yet Effective Data Augmentation Plugin for Sequential Recommendation
di: Dang, Yizhou, et al.
Pubblicazione: (2024)

Augmenting Sequential Recommendation with Balanced Relevance and Diversity
di: Dang, Yizhou, et al.
Pubblicazione: (2024)

Tail-Aware Data Augmentation for Long-Tail Sequential Recommendation
di: Dang, Yizhou, et al.
Pubblicazione: (2026)

Self-supervised Hierarchical Representation for Medication Recommendation
di: Liang, Yuliang, et al.
Pubblicazione: (2024)

Hard Negative Sampling via Large Language Models for Recommendation
di: Zhao, Chu, et al.
Pubblicazione: (2025)

Pay Attention to Sequence Split: Uncovering the Impacts of Sub-Sequence Splitting on Sequential Recommendation Models
di: Dang, Yizhou, et al.
Pubblicazione: (2026)

Towards Unified Modeling for Positive and Negative Preferences in Sign-Aware Recommendation
di: Liu, Yuting, et al.
Pubblicazione: (2024)

Causal Direct Preference Optimization for Distributionally Robust Generative Recommendation
di: Zhao, Chu, et al.
Pubblicazione: (2026)

Symmetric Graph Contrastive Learning against Noisy Views for Recommendation
di: Zhao, Chu, et al.
Pubblicazione: (2024)

ID Embedding as Subtle Features of Content and Structure for Multimodal Recommendation
di: Liu, Yuting, et al.
Pubblicazione: (2023)

CoRA: Collaborative Information Perception by Large Language Model's Weights for Recommendation
di: Liu, Yuting, et al.
Pubblicazione: (2024)

Graph Representation Learning via Causal Diffusion for Out-of-Distribution Recommendation
di: Zhao, Chu, et al.
Pubblicazione: (2024)

Fusion and Alignment Enhancement with Large Language Models for Tail-item Sequential Recommendation
di: Wei, Zhifu, et al.
Pubblicazione: (2026)

Generalized Embedding Machines for Recommender Systems
di: Yang, Enneng, et al.
Pubblicazione: (2020)

RAGAR: Retrieval Augmented Personalized Image Generation Guided by Recommendation
di: Ling, Run, et al.
Pubblicazione: (2025)

RaSeRec: Retrieval-Augmented Sequential Recommendation
di: Zhao, Xinping, et al.
Pubblicazione: (2024)

Retrieve-then-Adapt: Retrieval-Augmented Test-Time Adaptation for Sequential Recommendation
di: Tang, Xing, et al.
Pubblicazione: (2026)

Beyond One-Size-Fits-All: Adaptive Test-Time Augmentation for Sequential Recommendation
di: Li, Xibo, et al.
Pubblicazione: (2026)

Improving Sequential Recommenders through Counterfactual Augmentation of System Exposure
di: Zhao, Ziqi, et al.
Pubblicazione: (2025)

SSDRec: Self-Augmented Sequence Denoising for Sequential Recommendation
di: Zhang, Chi, et al.
Pubblicazione: (2024)

Intent-Enhanced Data Augmentation for Sequential Recommendation
di: Chen, Shuai, et al.
Pubblicazione: (2024)

Learnable Sequence Augmenter for Triplet Contrastive Learning in Sequential Recommendation
di: Wang, Wei, et al.
Pubblicazione: (2025)

LLMSeR: Enhancing Sequential Recommendation via LLM-based Data Augmentation
di: Sun, Yuqi, et al.
Pubblicazione: (2025)

Improving Sequential Recommendations via Bidirectional Temporal Data Augmentation with Pre-training
di: Jiang, Juyong, et al.
Pubblicazione: (2021)

Is Contrastive Learning Necessary? A Study of Data Augmentation vs Contrastive Learning in Sequential Recommendation
di: Zhou, Peilin, et al.
Pubblicazione: (2024)

AsarRec: Adaptive Sequential Augmentation for Robust Self-supervised Sequential Recommendation
di: Zhang, Kaike, et al.
Pubblicazione: (2025)

No Free Lunch: Retrieval-Augmented Generation Undermines Fairness in LLMs, Even for Vigilant Users
di: Hu, Mengxuan, et al.
Pubblicazione: (2024)

Generative Data Augmentation in Graph Contrastive Learning for Recommendation
di: Wang, Yansong, et al.
Pubblicazione: (2025)

Semantic Retrieval Augmented Contrastive Learning for Sequential Recommendation
di: Cui, Ziqiang, et al.
Pubblicazione: (2025)

Stealthy Attack on Large Language Model based Recommendation
di: Zhang, Jinghao, et al.
Pubblicazione: (2024)

ECHO: Entropy-Confidence Hybrid Optimization for Test-Time Reinforcement Learning
di: Zhao, Chu, et al.
Pubblicazione: (2026)

BLADE: A Behavior-Level Data Augmentation Framework with Dual Fusion Modeling for Multi-Behavior Sequential Recommendation
di: Li, Yupeng, et al.
Pubblicazione: (2025)

RAGR: Review-Augmented Generative Recommendation
di: Zhang, Yingyi, et al.
Pubblicazione: (2026)

i$^2$VAE: Interest Information Augmentation with Variational Regularizers for Cross-Domain Sequential Recommendation
di: Ning, Xuying, et al.
Pubblicazione: (2024)

SimAug: Enhancing Recommendation with Pretrained Language Models for Dense and Balanced Data Augmentation
di: Zhao, Yuying, et al.
Pubblicazione: (2025)

Dataset Regeneration for Sequential Recommendation
di: Yin, Mingjia, et al.
Pubblicazione: (2024)

Test-Time Alignment for Tracking User Interest Shifts in Sequential Recommendation
di: Zhang, Changshuo, et al.
Pubblicazione: (2025)

Reinforced Preference Optimization for Reasoning-Augmented Recommendations
di: Gao, Jingtong, et al.
Pubblicazione: (2026)

Large Language Models as Data Augmenters for Cold-Start Item Recommendation
di: Wang, Jianling, et al.
Pubblicazione: (2024)