Enhancing Bandit Algorithms with LLMs for Time-varying User Preferences in Streaming Recommendations
Fuente:
arXiv
Guardado en:
| Autores principales: | Shen, Chenglei, Zhan, Yi, Yu, Weijie, Zhang, Xiao, Xu, Jun |
|---|---|
| Formato: | Preprint |
| Publicado: |
2026
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Optimal Streaming Algorithms for Multi-Armed Bandits
por: Jin, Tianyuan, et al.
Publicado: (2024)
por: Jin, Tianyuan, et al.
Publicado: (2024)
IBCB: Efficient Inverse Batched Contextual Bandit for Behavioral Evolution History
por: Xu, Yi, et al.
Publicado: (2024)
por: Xu, Yi, et al.
Publicado: (2024)
Enhancing Preference-based Linear Bandits via Human Response Time
por: Li, Shen, et al.
Publicado: (2024)
por: Li, Shen, et al.
Publicado: (2024)
COURIER: Contrastive User Intention Reconstruction for Large-Scale Visual Recommendation
por: Yang, Jia-Qi, et al.
Publicado: (2023)
por: Yang, Jia-Qi, et al.
Publicado: (2023)
A Survey of Controllable Learning: Methods and Applications in Information Retrieval
por: Shen, Chenglei, et al.
Publicado: (2024)
por: Shen, Chenglei, et al.
Publicado: (2024)
Preference-centric Bandits: Optimality of Mixtures and Regret-efficient Algorithms
por: Tatlı, Meltem, et al.
Publicado: (2025)
por: Tatlı, Meltem, et al.
Publicado: (2025)
Multi-User Contextual Cascading Bandits for Personalized Recommendation
por: Park, Jiho, et al.
Publicado: (2025)
por: Park, Jiho, et al.
Publicado: (2025)
Contextual Bandit with Herding Effects: Algorithms and Recommendation Applications
por: Xu, Luyue, et al.
Publicado: (2024)
por: Xu, Luyue, et al.
Publicado: (2024)
Tweedie Regression for Video Recommendation System
por: Zheng, Yan, et al.
Publicado: (2025)
por: Zheng, Yan, et al.
Publicado: (2025)
Latent Preference Bandits
por: Mwai, Newton, et al.
Publicado: (2025)
por: Mwai, Newton, et al.
Publicado: (2025)
When Personalization Misleads: Understanding and Mitigating Hallucinations in Personalized LLMs
por: Sun, Zhongxiang, et al.
Publicado: (2026)
por: Sun, Zhongxiang, et al.
Publicado: (2026)
GenRecEdit: Adapting Model Editing for Generative Recommendation with Cold-Start Items
por: Shen, Chenglei, et al.
Publicado: (2026)
por: Shen, Chenglei, et al.
Publicado: (2026)
Provably Efficient Multi-Objective Bandit Algorithms under Preference-Centric Customization
por: Cao, Linfeng, et al.
Publicado: (2025)
por: Cao, Linfeng, et al.
Publicado: (2025)
Dynamic User Interest Augmentation via Stream Clustering and Memory Networks in Large-Scale Recommender Systems
por: Liu, Peng, et al.
Publicado: (2024)
por: Liu, Peng, et al.
Publicado: (2024)
Adapting Job Recommendations to User Preference Drift with Behavioral-Semantic Fusion Learning
por: Han, Xiao, et al.
Publicado: (2024)
por: Han, Xiao, et al.
Publicado: (2024)
Bayesian Bandit Algorithms with Approximate Inference in Stochastic Linear Bandits
por: Huang, Ziyi, et al.
Publicado: (2024)
por: Huang, Ziyi, et al.
Publicado: (2024)
Wasserstein Distributionally Robust Policy Evaluation and Learning for Contextual Bandits
por: Shen, Yi, et al.
Publicado: (2023)
por: Shen, Yi, et al.
Publicado: (2023)
The Bandit's Blind Spot: The Critical Role of User State Representation in Recommender Systems
por: Pires, Pedro R., et al.
Publicado: (2026)
por: Pires, Pedro R., et al.
Publicado: (2026)
Queueing Matching Bandits with Preference Feedback
por: Kim, Jung-hun, et al.
Publicado: (2024)
por: Kim, Jung-hun, et al.
Publicado: (2024)
Learning to Route and Schedule LLMs from User Retrials via Contextual Queueing Bandits
por: Bae, Seoungbin, et al.
Publicado: (2026)
por: Bae, Seoungbin, et al.
Publicado: (2026)
Modeling User Preferences as Distributions for Optimal Transport-Based Cross-Domain Recommendation under Non-Overlapping Settings
por: Xiao, Ziyin, et al.
Publicado: (2025)
por: Xiao, Ziyin, et al.
Publicado: (2025)
Unlocking Reasoning Capabilities in LLMs via Reinforcement Learning Exploration
por: Deng, Wenhao, et al.
Publicado: (2025)
por: Deng, Wenhao, et al.
Publicado: (2025)
Optimal and Practical Batched Linear Bandit Algorithm
por: Yu, Sanghoon, et al.
Publicado: (2025)
por: Yu, Sanghoon, et al.
Publicado: (2025)
The Nah Bandit: Modeling User Non-compliance in Recommendation Systems
por: Zhou, Tianyue, et al.
Publicado: (2024)
por: Zhou, Tianyue, et al.
Publicado: (2024)
On the Algorithmic Bias of Aligning Large Language Models with RLHF: Preference Collapse and Matching Regularization
por: Xiao, Jiancong, et al.
Publicado: (2024)
por: Xiao, Jiancong, et al.
Publicado: (2024)
Artificial Replay: A Meta-Algorithm for Harnessing Historical Data in Bandits
por: Banerjee, Siddhartha, et al.
Publicado: (2022)
por: Banerjee, Siddhartha, et al.
Publicado: (2022)
Algorithmic Assistance with Recommendation-Dependent Preferences
por: McLaughlin, Bryce, et al.
Publicado: (2022)
por: McLaughlin, Bryce, et al.
Publicado: (2022)
BanditSpec: Adaptive Speculative Decoding via Bandit Algorithms
por: Hou, Yunlong, et al.
Publicado: (2025)
por: Hou, Yunlong, et al.
Publicado: (2025)
Linear Bandits on Ellipsoids: Minimax Optimal Algorithms
por: Zhang, Raymond, et al.
Publicado: (2025)
por: Zhang, Raymond, et al.
Publicado: (2025)
Efficient Algorithms for Logistic Contextual Slate Bandits with Bandit Feedback
por: Goyal, Tanmay, et al.
Publicado: (2025)
por: Goyal, Tanmay, et al.
Publicado: (2025)
Save, Revisit, Retain: A Scalable Framework for Enhancing User Retention in Large-Scale Recommender Systems
por: Jiang, Weijie, et al.
Publicado: (2025)
por: Jiang, Weijie, et al.
Publicado: (2025)
Calibrated Recommendations with Contextual Bandits
por: Feijer, Diego, et al.
Publicado: (2025)
por: Feijer, Diego, et al.
Publicado: (2025)
Separating and Learning Latent Confounders to Enhancing User Preferences Modeling
por: Xu, Hangtong, et al.
Publicado: (2023)
por: Xu, Hangtong, et al.
Publicado: (2023)
Efficient and Interpretable Bandit Algorithms
por: Mukherjee, Subhojyoti, et al.
Publicado: (2023)
por: Mukherjee, Subhojyoti, et al.
Publicado: (2023)
Quantum-Enhanced Neural Contextual Bandit Algorithms
por: Huang, Yuqi, et al.
Publicado: (2026)
por: Huang, Yuqi, et al.
Publicado: (2026)
Do LLMs Benefit from User and Item Embeddings in Recommendation Tasks?
por: Hossain, Mir Rayat Imtiaz, et al.
Publicado: (2026)
por: Hossain, Mir Rayat Imtiaz, et al.
Publicado: (2026)
Aligning LLMs by Predicting Preferences from User Writing Samples
por: Aroca-Ouellette, Stéphane, et al.
Publicado: (2025)
por: Aroca-Ouellette, Stéphane, et al.
Publicado: (2025)
Harm Mitigation in Recommender Systems under User Preference Dynamics
por: Chee, Jerry, et al.
Publicado: (2024)
por: Chee, Jerry, et al.
Publicado: (2024)
FLDmamba: Integrating Fourier and Laplace Transform Decomposition with Mamba for Enhanced Time Series Prediction
por: Zhang, Qianru, et al.
Publicado: (2025)
por: Zhang, Qianru, et al.
Publicado: (2025)
Efficient and Adaptive Posterior Sampling Algorithms for Bandits
por: Hu, Bingshan, et al.
Publicado: (2024)
por: Hu, Bingshan, et al.
Publicado: (2024)
Ejemplares similares
-
Optimal Streaming Algorithms for Multi-Armed Bandits
por: Jin, Tianyuan, et al.
Publicado: (2024) -
IBCB: Efficient Inverse Batched Contextual Bandit for Behavioral Evolution History
por: Xu, Yi, et al.
Publicado: (2024) -
Enhancing Preference-based Linear Bandits via Human Response Time
por: Li, Shen, et al.
Publicado: (2024) -
COURIER: Contrastive User Intention Reconstruction for Large-Scale Visual Recommendation
por: Yang, Jia-Qi, et al.
Publicado: (2023) -
A Survey of Controllable Learning: Methods and Applications in Information Retrieval
por: Shen, Chenglei, et al.
Publicado: (2024)