行为增强的多层次协同Top-N推荐OA北大核心CSTPCD

Traditional recommendation systems only use one type of user behavior.However,multiple behaviors of users are related;therefore,ignoring user behaviors will result in the loss of the influence of auxiliary behavior on the target behavior.This paper proposes a multilevel collaborative Top-N recommendation based on enhanced user behavior(MCREB),which uses the attention mechanism to propagate information on the recommendation bipartite and item-based metapath graphs and learns multilevel high-order and heterogeneous collaborative signals,including user-item and inter-item,to improve recommendation performance.Thus,the model can better use the recommen-dation graph structure and fully consider the interaction between various behaviors on the recommendation graph structure.Furthermore,comprehensive experiments are conducted on the benchmark dataset to verify the model's effectiveness.