Recommendation method for fusion of knowledge graph convolutional network
Abstract In the application of internet of vehicles system, it is particularly important to obtain real-time and effective vehicle information and provide personalized functional services for vehicle operation. This algorithm combines knowledge graph technology with convolutional network and present...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2022-03-01
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| Series: | EURASIP Journal on Advances in Signal Processing |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13634-022-00854-7 |
| _version_ | 1819023061645524992 |
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| author | Xiaolin Jiang Yu Fu Changchun Dong |
| author_facet | Xiaolin Jiang Yu Fu Changchun Dong |
| author_sort | Xiaolin Jiang |
| collection | DOAJ |
| description | Abstract In the application of internet of vehicles system, it is particularly important to obtain real-time and effective vehicle information and provide personalized functional services for vehicle operation. This algorithm combines knowledge graph technology with convolutional network and presents a new algorithm model, that is, when calculating the representation of a given entity in the knowledge graph, the information of the neighboring entity is combined with the deviation. Through the integration of neighbor entity information, the local neighborhood structure can be better captured and stored in each entity, and the weight of different neighbor entities depends on the relationship between them and the specific user, which can better reflect the user's personalized interests, in order to fully demonstrate the characteristics of the entity. Compared with the traditional coordinated filtering technology SVD model, this model has improved accuracy and F1 value. |
| first_indexed | 2024-12-21T04:32:54Z |
| format | Article |
| id | doaj.art-e4d47b370fb2457584c6efa64a323d2d |
| institution | Directory Open Access Journal |
| issn | 1687-6180 |
| language | English |
| last_indexed | 2024-12-21T04:32:54Z |
| publishDate | 2022-03-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Advances in Signal Processing |
| spelling | doaj.art-e4d47b370fb2457584c6efa64a323d2d2022-12-21T19:15:54ZengSpringerOpenEURASIP Journal on Advances in Signal Processing1687-61802022-03-01202211910.1186/s13634-022-00854-7Recommendation method for fusion of knowledge graph convolutional networkXiaolin Jiang0Yu Fu1Changchun Dong2Jinhua Advanced Research InstituteHeilongjiang University of Science and TechnologyJinhua Advanced Research InstituteAbstract In the application of internet of vehicles system, it is particularly important to obtain real-time and effective vehicle information and provide personalized functional services for vehicle operation. This algorithm combines knowledge graph technology with convolutional network and presents a new algorithm model, that is, when calculating the representation of a given entity in the knowledge graph, the information of the neighboring entity is combined with the deviation. Through the integration of neighbor entity information, the local neighborhood structure can be better captured and stored in each entity, and the weight of different neighbor entities depends on the relationship between them and the specific user, which can better reflect the user's personalized interests, in order to fully demonstrate the characteristics of the entity. Compared with the traditional coordinated filtering technology SVD model, this model has improved accuracy and F1 value.https://doi.org/10.1186/s13634-022-00854-7Knowledge graphRecommended technologyConvolutional network |
| spellingShingle | Xiaolin Jiang Yu Fu Changchun Dong Recommendation method for fusion of knowledge graph convolutional network EURASIP Journal on Advances in Signal Processing Knowledge graph Recommended technology Convolutional network |
| title | Recommendation method for fusion of knowledge graph convolutional network |
| title_full | Recommendation method for fusion of knowledge graph convolutional network |
| title_fullStr | Recommendation method for fusion of knowledge graph convolutional network |
| title_full_unstemmed | Recommendation method for fusion of knowledge graph convolutional network |
| title_short | Recommendation method for fusion of knowledge graph convolutional network |
| title_sort | recommendation method for fusion of knowledge graph convolutional network |
| topic | Knowledge graph Recommended technology Convolutional network |
| url | https://doi.org/10.1186/s13634-022-00854-7 |
| work_keys_str_mv | AT xiaolinjiang recommendationmethodforfusionofknowledgegraphconvolutionalnetwork AT yufu recommendationmethodforfusionofknowledgegraphconvolutionalnetwork AT changchundong recommendationmethodforfusionofknowledgegraphconvolutionalnetwork |