Explicable recommendation based on knowledge graph
•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 Tra...
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| Vydané v: | Expert systems with applications Ročník 200; s. 117035 |
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| Hlavní autori: | , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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New York
Elsevier Ltd
15.08.2022
Elsevier BV |
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| ISSN: | 0957-4174, 1873-6793 |
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| Abstract | •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.
Most of the existing researches on recommendation system assemble in how to enhance precision of recommendation, ignoring acceptance and recognition of users. To work out the problem, a model of explainable recommendation on account of knowledge graph as well as many-objective evolutionary algorithm is proposed, which combines recommendation and explanation. In this work, embedding vectors obtained by embedding-based method are used to quantify the explainability, so as to obtain the explainability of paths between users and items. Candidate recommendation list of users is gained from constructed knowledge graph. Many-objective evolutionary algorithm is used to optimize the list of candidate recommendation so as to seek a set of tradeoff solutions to the four objective functions of accuracy, diversity, novelty and explainability. Then, the best path among object user and recommended items is chosen in knowledge graph as the explanation. Finally, the conclusion that can be drawn from various experiments is that the presented model can boost explainability without reducing the precision, diversity as well as novelty. |
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| AbstractList | •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.
Most of the existing researches on recommendation system assemble in how to enhance precision of recommendation, ignoring acceptance and recognition of users. To work out the problem, a model of explainable recommendation on account of knowledge graph as well as many-objective evolutionary algorithm is proposed, which combines recommendation and explanation. In this work, embedding vectors obtained by embedding-based method are used to quantify the explainability, so as to obtain the explainability of paths between users and items. Candidate recommendation list of users is gained from constructed knowledge graph. Many-objective evolutionary algorithm is used to optimize the list of candidate recommendation so as to seek a set of tradeoff solutions to the four objective functions of accuracy, diversity, novelty and explainability. Then, the best path among object user and recommended items is chosen in knowledge graph as the explanation. Finally, the conclusion that can be drawn from various experiments is that the presented model can boost explainability without reducing the precision, diversity as well as novelty. Most of the existing researches on recommendation system assemble in how to enhance precision of recommendation, ignoring acceptance and recognition of users. To work out the problem, a model of explainable recommendation on account of knowledge graph as well as many-objective evolutionary algorithm is proposed, which combines recommendation and explanation. In this work, embedding vectors obtained by embedding-based method are used to quantify the explainability, so as to obtain the explainability of paths between users and items. Candidate recommendation list of users is gained from constructed knowledge graph. Many-objective evolutionary algorithm is used to optimize the list of candidate recommendation so as to seek a set of tradeoff solutions to the four objective functions of accuracy, diversity, novelty and explainability. Then, the best path among object user and recommended items is chosen in knowledge graph as the explanation. Finally, the conclusion that can be drawn from various experiments is that the presented model can boost explainability without reducing the precision, diversity as well as novelty. |
| ArticleNumber | 117035 |
| Author | Cui, Zhihua Cai, Xingjuan Xie, Lijie Tian, Rui |
| Author_xml | – sequence: 1 givenname: Xingjuan surname: Cai fullname: Cai, Xingjuan email: xingjuancai@163.com – sequence: 2 givenname: Lijie surname: Xie fullname: Xie, Lijie email: lijiexie11@163.com – sequence: 3 givenname: Rui surname: Tian fullname: Tian, Rui email: ruitianwz@163.com – sequence: 4 givenname: Zhihua surname: Cui fullname: Cui, Zhihua email: cuizhihua@tyust.edu.cn |
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| Keywords | Explainable recommendation Unified method Many-objective evolutionary algorithm Knowledge graph Recommendation system |
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| Snippet | •Explainable recommendation model is proposed to improve recommended performance.•Precision, diversity, novelty and explainability are optimized... Most of the existing researches on recommendation system assemble in how to enhance precision of recommendation, ignoring acceptance and recognition of users.... |
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| SubjectTerms | Embedding Evolutionary algorithms Explainable recommendation Genetic algorithms Knowledge graph Knowledge representation Many-objective evolutionary algorithm Recommendation system Recommender systems Unified method |
| Title | Explicable recommendation based on knowledge graph |
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