A deep reinforcement learning based long-term recommender system

Recommender systems aim to maximize the overall accuracy for long-term recommendations. However, most of the existing recommendation models adopt a static view, and ignore the fact that recommendation is a dynamic sequential decision-making process. As a result, they fail to adapt to new situations and suffer from the cold-start problem. Although sequential recommendation methods have been gaining attention recently, the objective of long-term...