A5078799714- Auction Theory and Applications
- Consumer Market Behavior and Pricing
- Advanced Bandit Algorithms Research
- Reinforcement Learning in Robotics
- Transportation and Mobility Innovations
- Recommender Systems and Techniques
We study the budget allocation problem in online marketing campaigns that utilize previously collected offline data. first discuss long-term effect of optimizing decisions setting. To overcome challenge, we propose a novel game-theoretic value-based reinforcement learning method using mixed policies. The proposed reduces need to store infinitely many policies previous methods only constantly policies, which achieves nearly optimal policy efficiency, making it practical and favorable for...
We study the budget allocation problem in online marketing campaigns that utilize previously collected offline data. first discuss long-term effect of optimizing decisions setting. To overcome challenge, we propose a novel game-theoretic value-based reinforcement learning method using mixed policies. The proposed reduces need to store infinitely many policies previous methods only constantly policies, which achieves nearly optimal policy efficiency, making it practical and favorable for...
Model-free RL-based recommender systems have recently received increasing research attention due to their capability handle partial feedback and long-term rewards. However, most existing has ignored a critical feature in systems: one user's on the same item at different times is random. The stochastic rewards property essentially differs from that classic RL scenarios with deterministic rewards, which makes much more challenging. In this paper, we first demonstrate simulator environment...