Open AccessA novel recommendation algorithm with knowledge graph
Author(s) -
L. Liu,
PengFei Zhang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1812/1/012035
Subject(s) - computer science , collaborative filtering , graph , recommender system , knowledge graph , similarity (geometry) , cold start (automotive) , ranking (information retrieval) , subgraph isomorphism problem , algorithm , machine learning , data mining , theoretical computer science , artificial intelligence , engineering , image (mathematics) , aerospace engineering
Personalized recommendation is an important topic in recommendation algorithm research. Traditional collaborative filtering recommendation algorithms suffer the sparseness and cold start problems. Existing knowledge graph based recommendation algorithms miss the high-order similarity at the subgraph level. This paper introduces a RNN-based distributed representation model of knowledge graph called KG-GRU, which uses a sequence containing nodes and relationships to model the subgraph similarity in the same embedded vector space. Then, a personalized recommendation algorithm based on knowledge graph called KG-GRU4Rec, is proposed. KG-GRU4Rec implements an end-to-end predictive user rating model. Experimental results demonstrate that KG-GRU learns more accurate knowledge about entities and relationships in the graph and KG-GRU4Rec outperforms the comparison algorithm in both hit ratio and average reciprocal ranking.