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Interactive Recommendation
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논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Contrastive Representation for Interactive Recommendation
InteraRec: Screenshot Based Recommendations Using Multimodal Large Language Models
Debiased Model-based Interactive Recommendation
A General Neural Causal Model for Interactive Recommendation
Adversarial Batch Inverse Reinforcement Learning: Learn to Reward from Imperfect Demonstration for Interactive Recommendation
Preference Elicitation with Soft Attributes in Interactive Recommendation
A General Offline Reinforcement Learning Framework for Interactive Recommendation
Towards Validating Long-Term User Feedbacks in Interactive Recommendation Systems
Alleviating Matthew Effect of Offline Reinforcement Learning in Interactive Recommendation
Triple Structural Information Modelling for Accurate, Explainable and Interactive Recommendation
Digital Human Interactive Recommendation Decision-Making Based on Reinforcement Learning
Dynamic Global Sensitivity for Differentially Private Contextual Bandits
KuaiRand: An Unbiased Sequential Recommendation Dataset with Randomly Exposed Videos
Contrastive Learning for Interactive Recommendation in Fashion
Modelling Users with Item Metadata for Explainable and Interactive Recommendation
CIRS: Bursting Filter Bubbles by Counterfactual Interactive Recommender System
Model-agnostic Counterfactual Synthesis Policy for Interactive Recommendation
Adversarial Robustness of Deep Reinforcement Learning based Dynamic Recommender Systems
D2RLIR : an improved and diversified ranking function in interactive recommendation systems based on deep reinforcement learning
A Survey on Reinforcement Learning for Recommender Systems
The Use of Bandit Algorithms in Intelligent Interactive Recommender Systems
Quantifying Availability and Discovery in Recommender Systems via Stochastic Reachability
Balancing Accuracy and Fairness for Interactive Recommendation with Reinforcement Learning
When and Whom to Collaborate with in a Changing Environment: A Collaborative Dynamic Bandit Solution
A Hybrid Bandit Framework for Diversified Recommendation
Adversarial Attacks and Detection on Reinforcement Learning-Based Interactive Recommender Systems
Reward Constrained Interactive Recommendation with Natural Language Feedback
Knowledge-guided Deep Reinforcement Learning for Interactive Recommendation
A Text-based Deep Reinforcement Learning Framework for Interactive Recommendation
IRF: Interactive Recommendation through Dialogue
RecSim: A Configurable Simulation Platform for Recommender Systems
Bandit Learning for Diversified Interactive Recommendation
Query-based Interactive Recommendation by Meta-Path and Adapted Attention-GRU
Diversity-Promoting Deep Reinforcement Learning for Interactive Recommendation
When Collaborative Filtering Meets Reinforcement Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.