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Interactive Recommendation

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Showing 1–25 of 40 papers

TitleStatusHype
Thought-Augmented Planning for LLM-Powered Interactive Recommender AgentCode0
Contrastive Representation for Interactive RecommendationCode0
InteraRec: Screenshot Based Recommendations Using Multimodal Large Language Models—0
Debiased Model-based Interactive Recommendation—0
A General Neural Causal Model for Interactive Recommendation—0
Adversarial Batch Inverse Reinforcement Learning: Learn to Reward from Imperfect Demonstration for Interactive Recommendation—0
Preference Elicitation with Soft Attributes in Interactive Recommendation—0
A General Offline Reinforcement Learning Framework for Interactive Recommendation—0
Towards Validating Long-Term User Feedbacks in Interactive Recommendation Systems—0
Alleviating Matthew Effect of Offline Reinforcement Learning in Interactive RecommendationCode1
Triple Structural Information Modelling for Accurate, Explainable and Interactive Recommendation—0
Digital Human Interactive Recommendation Decision-Making Based on Reinforcement Learning—0
Dynamic Global Sensitivity for Differentially Private Contextual Bandits—0
KuaiRand: An Unbiased Sequential Recommendation Dataset with Randomly Exposed VideosCode1
Contrastive Learning for Interactive Recommendation in Fashion—0
Modelling Users with Item Metadata for Explainable and Interactive RecommendationCode0
CIRS: Bursting Filter Bubbles by Counterfactual Interactive Recommender SystemCode1
Model-agnostic Counterfactual Synthesis Policy for Interactive Recommendation—0
Adversarial Robustness of Deep Reinforcement Learning based Dynamic Recommender Systems—0
D2RLIR : an improved and diversified ranking function in interactive recommendation systems based on deep reinforcement learning—0
A Survey on Reinforcement Learning for Recommender Systems—0
The Use of Bandit Algorithms in Intelligent Interactive Recommender Systems—0
Quantifying Availability and Discovery in Recommender Systems via Stochastic ReachabilityCode0
Balancing Accuracy and Fairness for Interactive Recommendation with Reinforcement Learning—0
When and Whom to Collaborate with in a Changing Environment: A Collaborative Dynamic Bandit Solution—0
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