Balancing Accuracy and Fairness for Interactive Recommendation with Reinforcement Learning.
Weiwen LiuFeng LiuRuiming TangBen LiaoGuangyong ChenPheng-Ann HengPublished in: CoRR (2021)
Keyphrases
- reinforcement learning
- high accuracy
- user interaction
- machine learning
- user friendly
- recommender systems
- computational cost
- state space
- recommendation algorithms
- prediction accuracy
- computer graphics
- highly accurate
- collaborative filtering
- user preferences
- virtual reality
- resource allocation
- high precision
- temporal difference
- neural network
- function approximation
- robotic control
- computational efficiency
- error rate
- classification accuracy
- computational complexity
- learning algorithm
- information retrieval