Recommendation model combining review’s feature and rating graph convolutional representation
In order to fully exploit the effective information of the ratings and further investigate the importance of the review, a recommendation model combining review’s feature and rating graph convolutional representation was proposed.Graph convolutional neural network was used to learn the representatio...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2022-03-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2022049/ |
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| _version_ | 1841540004804820992 |
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| author | Hailin FENG Xiao ZHANG Tongcun LIU |
| author_facet | Hailin FENG Xiao ZHANG Tongcun LIU |
| author_sort | Hailin FENG |
| collection | DOAJ |
| description | In order to fully exploit the effective information of the ratings and further investigate the importance of the review, a recommendation model combining review’s feature and rating graph convolutional representation was proposed.Graph convolutional neural network was used to learn the representation of user and item from the ratings data.Combining with text convolutional features, attention mechanism was utilized to distinguish the importance of the review.Finally, the representation learned from the review and the rating data was fused by the hidden factor model.The experimental results on Amazon’s public data showed that the proposed model significantly outperformed the traditional approaches, proving the effectiveness of the proposed model. |
| format | Article |
| id | doaj-art-f74c2f0a19784262a353e0b7fe027831 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2022-03-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-f74c2f0a19784262a353e0b7fe0278312025-01-14T06:29:10ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2022-03-014316417159393078Recommendation model combining review’s feature and rating graph convolutional representationHailin FENGXiao ZHANGTongcun LIUIn order to fully exploit the effective information of the ratings and further investigate the importance of the review, a recommendation model combining review’s feature and rating graph convolutional representation was proposed.Graph convolutional neural network was used to learn the representation of user and item from the ratings data.Combining with text convolutional features, attention mechanism was utilized to distinguish the importance of the review.Finally, the representation learned from the review and the rating data was fused by the hidden factor model.The experimental results on Amazon’s public data showed that the proposed model significantly outperformed the traditional approaches, proving the effectiveness of the proposed model.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2022049/recommender modelgraph convolutional encoderattention mechanismlatent factor model |
| spellingShingle | Hailin FENG Xiao ZHANG Tongcun LIU Recommendation model combining review’s feature and rating graph convolutional representation Tongxin xuebao recommender model graph convolutional encoder attention mechanism latent factor model |
| title | Recommendation model combining review’s feature and rating graph convolutional representation |
| title_full | Recommendation model combining review’s feature and rating graph convolutional representation |
| title_fullStr | Recommendation model combining review’s feature and rating graph convolutional representation |
| title_full_unstemmed | Recommendation model combining review’s feature and rating graph convolutional representation |
| title_short | Recommendation model combining review’s feature and rating graph convolutional representation |
| title_sort | recommendation model combining review s feature and rating graph convolutional representation |
| topic | recommender model graph convolutional encoder attention mechanism latent factor model |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2022049/ |
| work_keys_str_mv | AT hailinfeng recommendationmodelcombiningreviewsfeatureandratinggraphconvolutionalrepresentation AT xiaozhang recommendationmodelcombiningreviewsfeatureandratinggraphconvolutionalrepresentation AT tongcunliu recommendationmodelcombiningreviewsfeatureandratinggraphconvolutionalrepresentation |