哈尔滨工程大学学报2024，Vol.45Issue(6)：1119-1126,8.DOI:10.11990/jheu.202204020
行为增强的多层次协同Top-N推荐
Multilevel collaborative top-n recommendation based on enhanced behavior
摘要
Abstract
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.关键词
辅助行为/多行为/图神经网络/元路径图/用户-项目/传播层/目标行为/高阶异质信号Key words
auxiliary behavior/multibehavior/graph neural network/metapath graph/user-item/propagation lay-er/target behavior/high-order heterogenous signal分类
信息技术与安全科学引用本文复制引用
刘宇鹏,吕衍河..行为增强的多层次协同Top-N推荐[J].哈尔滨工程大学学报,2024,45(6):1119-1126,8.基金项目
国家自然科学基金项目(61300115) （61300115）
中国博士后科学基金项目(2014m561331) （2014m561331）
黑龙江省教育厅科学技术研究项目(12521073). （12521073）
