摘要
In view of the low interpretability of existing collaborative filtering recommendation algorithms and the difficulty of extracting information from content-based recommendation algorithms,we propose an efficient KGRS model.KGRS first obtains reasoning paths of knowledge graph and embeds the entities of paths into vectors based on knowledge representation learning TransD algorithm,then uses LSTM and soft attention mechanism to capture the semantic of each path reasoning,then uses convolution operation and pooling operation to distinguish the importance of different paths reasoning.Finally,through the full connection layer and sigmoid function to get the prediction ratings,and the items are sorted according to the prediction ratings to get the user’s recommendation list.KGRS is tested on the movielens-100k dataset.Compared with the related representative algorithm,including the state-of-the-art interpretable recommendation models RKGE and RippleNet,the experimental results show that KGRS has good recommendation interpretation and higher recommendation accuracy.
基金
supported by the National Science Foundation of China Grant No.61762092
“Dynamic multi-objective requirement optimization based on transfer learning”,No.61762089
“The key research of high order tensor decomposition in distributed environment”
the Open Foundation of the Key Laboratory in Software Engineering of Yunnan Province,Grant No.2017SE204,”
Research on extracting software feature models using transfer learning”.