Cover Image

Causal Feature Selection Method for Contextual Multi-Armed Bandits in Recommender System

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Zhao, Zhenyu, Jiang, Yexi
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Information Retrieval
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Calibrated Recommendations with Contextual Bandits
by: Feijer, Diego, et al.
Published: (2025)

Algorithm Adaptation Bias in Recommendation System Online Experiments
by: Zheng, Chen, et al.
Published: (2025)

Contextual Bandit with Herding Effects: Algorithms and Recommendation Applications
by: Xu, Luyue, et al.
Published: (2024)

Modeling Attrition in Recommender Systems with Departing Bandits
by: Ben-Porat, Omer, et al.
Published: (2022)

FairFS: Addressing Deep Feature Selection Biases for Recommender System
by: Wang, Xianquan, et al.
Published: (2026)

Optimal Baseline Corrections for Off-Policy Contextual Bandits
by: Gupta, Shashank, et al.
Published: (2024)

The Bandit's Blind Spot: The Critical Role of User State Representation in Recommender Systems
by: Pires, Pedro R., et al.
Published: (2026)

Retrievable Domain-Sensitive Feature Memory for Multi-Domain Recommendation
by: Zhao, Yuang, et al.
Published: (2024)

Contextual Bandits in Payment Processing: Non-uniform Exploration and Supervised Learning
by: Vangara, Akhila, et al.
Published: (2024)

A Unified Causal Framework for Auditing Recommender Systems for Ethical Concerns
by: Sharma, Vibhhu, et al.
Published: (2024)

Taming Recommendation Bias with Causal Intervention on Evolving Personal Popularity
by: Tan, Shiyin, et al.
Published: (2025)

Maximum Impact with Fewer Features: Efficient Feature Selection for Cold-Start Recommenders through Collaborative Importance Weighting
by: Sukhorukov, Nikita, et al.
Published: (2025)

Context Features Are Cheap: Rank-Aware Decomposition for Efficient Feature Interaction in Recommender Systems
by: Tkach, Yevgeny
Published: (2026)

ActionPiece: Contextually Tokenizing Action Sequences for Generative Recommendation
by: Hou, Yupeng, et al.
Published: (2025)

Adaptive Conditional Expert Selection Network for Multi-domain Recommendation
by: Dong, Kuiyao, et al.
Published: (2024)

DTN: Deep Multiple Task-specific Feature Interactions Network for Multi-Task Recommendation
by: Bi, Yaowen, et al.
Published: (2024)

Exploitation Over Exploration: Unmasking the Bias in Linear Bandit Recommender Offline Evaluation
by: Pires, Pedro R., et al.
Published: (2025)

Multi-task Offline Reinforcement Learning for Online Advertising in Recommender Systems
by: Liu, Langming, et al.
Published: (2025)

Causality-aware Graph Aggregation Weight Estimator for Popularity Debiasing in Top-K Recommendation
by: Que, Yue, et al.
Published: (2025)

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

AdaptEx: A Self-Service Contextual Bandit Platform
by: Black, William, et al.
Published: (2023)

EnhancedRL: An Enhanced-State Reinforcement Learning Algorithm for Multi-Task Fusion in Recommender Systems
by: Liu, Peng, et al.
Published: (2024)

CASP: Support-Aware Offline Policy Selection for Two-Stage Recommender Systems
by: Chapagain, Nilson
Published: (2026)

Knowledge-Aware Multi-Intent Contrastive Learning for Multi-Behavior Recommendation
by: Liang, Shunpan, et al.
Published: (2024)

Multi-Margin Cosine Loss: Proposal and Application in Recommender Systems
by: Ozsoy, Makbule Gulcin
Published: (2024)

ACT: Automated Constraint Targeting for Multi-Objective Recommender Systems
by: Chang, Daryl, et al.
Published: (2025)

Enhancing Interpretability and Effectiveness in Recommendation with Numerical Features via Learning to Contrast the Counterfactual samples
by: Xu, Xiaoxiao, et al.
Published: (2025)

CSRec: Rethinking Sequential Recommendation from A Causal Perspective
by: Liu, Xiaoyu, et al.
Published: (2024)

Agent0: Leveraging LLM Agents to Discover Multi-value Features from Text for Enhanced Recommendations
by: Škrlj, Blaž, et al.
Published: (2025)

STEM: Unleashing the Power of Embeddings for Multi-task Recommendation
by: Su, Liangcai, et al.
Published: (2023)

UnifiedRL: A Reinforcement Learning Algorithm Tailored for Multi-Task Fusion in Large-Scale Recommender Systems
by: Liu, Peng, et al.
Published: (2024)

Intelligent Algorithm Selection for Recommender Systems: Meta-Learning via in-depth algorithm feature engineering
by: Decker, Jarne Mathi
Published: (2025)

Policy-Guided Causal State Representation for Offline Reinforcement Learning Recommendation
by: Wang, Siyu, et al.
Published: (2025)

Balanced Co-Clustering of Users and Items for Embedding Table Compression in Recommender Systems
by: Jiang, Runhao, et al.
Published: (2026)

Performative Debias with Fair-exposure Optimization Driven by Strategic Agents in Recommender Systems
by: Xiang, Zhichen, et al.
Published: (2024)

Continual Recommender Systems
by: Yoo, Hyunsik, et al.
Published: (2025)

Knowledge Enhanced Multi-Domain Recommendations in an AI Assistant Application
by: Markowitz, Elan, et al.
Published: (2023)

Unified Representation Learning for Multi-Intent Diversity and Behavioral Uncertainty in Recommender Systems
by: Xu, Wei, et al.
Published: (2025)

The Nah Bandit: Modeling User Non-compliance in Recommendation Systems
by: Zhou, Tianyue, et al.
Published: (2024)

Contrastive Learning for Cold Start Recommendation with Adaptive Feature Fusion
by: Hu, Jiacheng, et al.
Published: (2025)