Reinforcement learning over sentiment-augmented knowledge graphs towards accurate and explainable recommendation
DC Field | Value | Language |
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dc.contributor.author | Park, Sung-Jun | - |
dc.contributor.author | Chae, Dong Kyu | - |
dc.contributor.author | Bae, Hong-Kyun | - |
dc.contributor.author | Park, Sumin | - |
dc.contributor.author | Kim, Sang-Wook | - |
dc.date.accessioned | 2022-07-06T10:15:25Z | - |
dc.date.available | 2022-07-06T10:15:25Z | - |
dc.date.created | 2022-04-06 | - |
dc.date.issued | 2022-02 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/139513 | - |
dc.description.abstract | Explainable recommendation has gained great attention in recent years. A lot of work in this research line has chosen to use the knowledge graphs (KG) where relations between entities can serve as explanations. However, existing studies have not considered sentiment on relations in KG, although there can be various types of sentiment on relations worth considering (e.g., a user's satisfaction on an item). In this paper, we propose a novel recommendation framework based on KG integrated with sentiment analysis for more accurate recommendation as well as more convincing explanations. To this end, we first construct a Sentiment-Aware Knowledge Graph (namely, SAKG) by analyzing reviews and ratings on items given by users. Then, we perform item recommendation and reasoning over SAKG through our proposed Sentiment-Aware Policy Learning (namely, SAPL) based on a reinforcement learning strategy. To enhance the explainability for end-users, we further developed an interactive user interface presenting textual explanations as well as a collection of reviews related with the discovered sentiment. Experimental results on three real-world datasets verified clear improvements on both the accuracy of recommendation and the quality of explanations. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Association for Computing Machinery, Inc | - |
dc.title | Reinforcement learning over sentiment-augmented knowledge graphs towards accurate and explainable recommendation | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Chae, Dong Kyu | - |
dc.contributor.affiliatedAuthor | Kim, Sang-Wook | - |
dc.identifier.doi | 10.1145/3488560.3498515 | - |
dc.identifier.scopusid | 2-s2.0-85125791458 | - |
dc.identifier.wosid | 000810504300085 | - |
dc.identifier.bibliographicCitation | WSDM 2022 - Proceedings of the 15th ACM International Conference on Web Search and Data Mining, pp.784 - 793 | - |
dc.relation.isPartOf | WSDM 2022 - Proceedings of the 15th ACM International Conference on Web Search and Data Mining | - |
dc.citation.title | WSDM 2022 - Proceedings of the 15th ACM International Conference on Web Search and Data Mining | - |
dc.citation.startPage | 784 | - |
dc.citation.endPage | 793 | - |
dc.type.rims | ART | - |
dc.type.docType | Proceedings Paper | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Theory & Methods | - |
dc.subject.keywordPlus | Knowledge graph | - |
dc.subject.keywordPlus | Reinforcement learning | - |
dc.subject.keywordPlus | User interfaces | - |
dc.subject.keywordPlus | Sentiment analysis | - |
dc.subject.keywordPlus | End-users | - |
dc.subject.keywordPlus | Explainable recommendation | - |
dc.subject.keywordPlus | Interactive user interfaces | - |
dc.subject.keywordPlus | Knowledge graphs | - |
dc.subject.keywordPlus | Policy learning | - |
dc.subject.keywordPlus | Real-world datasets | - |
dc.subject.keywordPlus | Sentiment analysis | - |
dc.subject.keywordPlus | Users&apos | - |
dc.subject.keywordPlus | satisfactions | - |
dc.subject.keywordAuthor | Explainable recommendation | - |
dc.subject.keywordAuthor | Knowledge graph | - |
dc.subject.keywordAuthor | Sentiment analysis | - |
dc.identifier.url | https://dl.acm.org/doi/10.1145/3488560.3498515 | - |
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