Explainable recommendation based on knowledge graph and multi-objective optimization
文献类型：期刊论文
| 作者 | Xie, Lijie1; Hu, Zhaoming1; Cai, Xingjuan1; Zhang, Wensheng3 ; Chen, Jinjun2
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| 刊名 | COMPLEX & INTELLIGENT SYSTEMS
 |
| 出版日期 | 2021-03-06 |
| 页码 | 12 |
| 关键词 | Recommendation system Knowledge graph Multi-objective optimization Explainability |
| ISSN号 | 2199-4536 |
| DOI | 10.1007/s40747-021-00315-y |
| 通讯作者 | Cai, Xingjuan(xingjuancai@163.com) |
| 英文摘要 | Recommendation system is a technology that can mine user's preference for items. Explainable recommendation is to produce recommendations for target users and give reasons at the same time to reveal reasons for recommendations. The explainability of recommendations that can improve the transparency of recommendations and the probability of users choosing the recommended items. The merits about explainability of recommendations are obvious, but it is not enough to focus solely on explainability of recommendations in field of explainable recommendations. Therefore, it is essential to construct an explainable recommendation framework to improve the explainability of recommended items while maintaining accuracy and diversity. An explainable recommendation framework based on knowledge graph and multi-objective optimization is proposed that can optimize the precision, diversity and explainability about recommendations at the same time. Knowledge graph connects users and items through different relationships to obtain an explainable candidate list for target user, and the path between target user and recommended item is used as an explanation basis. The explainable candidate list is optimized through multi-objective optimization algorithm to obtain the final recommendation list. It is concluded from the results about experiments that presented explainable recommendation framework provides high-quality recommendations that contains high accuracy, diversity and explainability. |
| 资助项目 | National Key Research and Development Program of China[2018YFC1604000] ; National Natural Science Foundation of China[61806138] ; National Natural Science Foundation of China[U1636220] ; National Natural Science Foundation of China[61961160707] ; National Natural Science Foundation of China[61976212] ; Key R&D program of Shanxi Province (International Cooperation)[201903D421048] ; Australian Research Council (ARC)[DP190101893] ; Australian Research Council (ARC)[DP170100136] ; Australian Research Council (ARC)[LP180100758] |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000625746400001 |
| 出版者 | SPRINGER HEIDELBERG |
| 资助机构 | National Key Research and Development Program of China ; National Natural Science Foundation of China ; Key R&D program of Shanxi Province (International Cooperation) ; Australian Research Council (ARC) |
| 源URL | [http://ir.ia.ac.cn/handle/173211/44164]  |
| 专题 | 精密感知与控制研究中心_人工智能与机器学习 |
| 通讯作者 | Cai, Xingjuan |
| 作者单位 | 1.Taiyuan Univ Sci & Technol, Sch Comp Sci & Technol, Taiyuan, Peoples R China 2.Swinburne Univ Technol, Melbourne, Vic, Australia 3.Chinese Acad Sci, Inst Automat, State Key Lab Intelligent Control & Management Co, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Xie, Lijie,Hu, Zhaoming,Cai, Xingjuan,et al. Explainable recommendation based on knowledge graph and multi-objective optimization[J]. COMPLEX & INTELLIGENT SYSTEMS,2021:12. |
| APA | Xie, Lijie,Hu, Zhaoming,Cai, Xingjuan,Zhang, Wensheng,&Chen, Jinjun.(2021).Explainable recommendation based on knowledge graph and multi-objective optimization.COMPLEX & INTELLIGENT SYSTEMS,12. |
| MLA | Xie, Lijie,et al."Explainable recommendation based on knowledge graph and multi-objective optimization".COMPLEX & INTELLIGENT SYSTEMS (2021):12. |
入库方式： OAI收割
来源：自动化研究所
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