An explainable recommendation framework based on an improved knowledge graph attention network with massive volumes of side information
作者：
Highlights：
• Reduced the computational cost of knowledge graph enabled recommendation.
• Improved explainability while maintaining high recommendation accuracy.
• Introduced how to apply knowledge graph embedding to marketing strategy planning.
• Presented strengths of explainable recommendation, not just explainability.
• Improved value and applicability of knowledge graph attention network to services.
摘要
•Reduced the computational cost of knowledge graph enabled recommendation.•Improved explainability while maintaining high recommendation accuracy.•Introduced how to apply knowledge graph embedding to marketing strategy planning.•Presented strengths of explainable recommendation, not just explainability.•Improved value and applicability of knowledge graph attention network to services.
论文关键词：00-01,99-00,Explainable artificial intelligence,Explainable recommendation,Model-intrinsic approach,Knowledge graph attention network,Knowledge graph embedding,Knowledge graph enabled recommendation
论文评审过程：Received 11 September 2021, Revised 28 November 2021, Accepted 12 December 2021, Available online 21 December 2021, Version of Record 5 January 2022.
论文官网地址：https://doi.org/10.1016/j.knosys.2021.107970
