Explicable recommendation based on knowledge graph
作者：
Highlights：
• Explainable recommendation model is proposed to improve recommended performance.
• Precision, diversity, novelty and explainability are optimized simultaneously.
• The list of candidate recommendation is gained through knowledge graph.
• Embedding vectors of entities and relationships are obtained by TransH.
• Unified method is used to quantify explainability.
摘要
•Explainable recommendation model is proposed to improve recommended performance.•Precision, diversity, novelty and explainability are optimized simultaneously.•The list of candidate recommendation is gained through knowledge graph.•Embedding vectors of entities and relationships are obtained by TransH.•Unified method is used to quantify explainability.
论文关键词：Explainable recommendation,Knowledge graph,Unified method,Recommendation system,Many-objective evolutionary algorithm
论文评审过程：Received 5 October 2021, Revised 4 January 2022, Accepted 27 March 2022, Available online 31 March 2022, Version of Record 25 April 2022.
论文官网地址：https://doi.org/10.1016/j.eswa.2022.117035
