The invention provides a deep
reinforcement learning interactive recommendation
system and method based on knowledge enhancement, and relates to the technical field of recommendation. The
system comprises a
data acquisition and cleaning module, an environment simulator construction module, a
knowledge graph construction module, a graph
convolution module, a
user state representation module, a strategy
network module and a
value network module. According to the method, rich
semantic information in a
knowledge graph is combined, a graph convolutional
network structure is utilized, embedded representation of adjacent entities is propagated recursively along high-order
connectivity, a graph
attention network thought is adopted, item representation is enhanced by utilizing the rich
semantic information in the
knowledge graph, and meanwhile, a user-item
bipartite graph is fused, so that the method is more efficient and efficient. The potential relationship is fully mined from collective user behaviors, so that the dynamic preference of the user is accurately captured, and the optimal recommendation strategy is autonomously learned by using deep
reinforcement learning, so that the recommendation accuracy is improved.