Hierarchical Reinforcement Learning Recommendation Method based on Deep Interest Networks

With the rapid development of MOOC platforms, course recommendation has become a focal point in the research field of recommendations. Effectively modeling user preferences is a crucial task within this context. Despite the notable achievements of reinforcement learning methods in simulating user preferences, there are still existing issues with current approaches. This paper introduces a hierarchical reinforcement learning method based on deep interest networks. By incorporating an adaptive weighting unit to represent the correlation between the current course and historical courses, the method aims to better simulate user preferences. Experimental results on two real datasets demonstrate the superiority of our approach over baseline methods, and it exhibits strong generalization in other recommendation domains.