Qing Da

Stabilizing Reinforcement Learning in Dynamic Environment with Application to Online Recommendation

2018

Deep reinforcement learning has shown great potential in improving system performance autonomously, by learning from iterations with the environment. However, traditional reinforcement learning approaches are designed to work in static environments. In many real-world problems, …

Diversity Regularized Interests Modeling for Recommender Systems

2021 · arXiv (Cornell University)

With the rapid development of E-commerce and the increase in the quantity of items, users are presented with more items hence their interests broaden. It is increasingly difficult to model user intentions with traditional methods, …