Hybrid Recommendation Model of Knowledge Graph and Graph Convolutional Network
Concerning the failure of most current recommendation models based on knowledge graph to adequately model users’ characteristics, the neighborhood relationship between entities in the knowledge graph is not considered. This paper proposes a hybrid recommendation model that combines knowl...
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press
2022-06-01
|
| Series: | Jisuanji kexue yu tansuo |
| Subjects: | |
| Online Access: | http://fcst.ceaj.org/fileup/1673-9418/PDF/2110057.pdf |
| _version_ | 1811241798423543808 |
|---|---|
| author | GUO Xiaowang, XIA Hongbin, LIU Yuan |
| author_facet | GUO Xiaowang, XIA Hongbin, LIU Yuan |
| author_sort | GUO Xiaowang, XIA Hongbin, LIU Yuan |
| collection | DOAJ |
| description | Concerning the failure of most current recommendation models based on knowledge graph to adequately model users’ characteristics, the neighborhood relationship between entities in the knowledge graph is not considered. This paper proposes a hybrid recommendation model that combines knowledge graph and graph convolutional network (HKC). Firstly, the KGCN (knowledge graph convolutional networks for recommender systems) algorithm is used to capture the correlation between items, and obtain the feature vector of the item through neighborhood aggregation unit. The entities associated with the user in the knowledge graph are extracted through collaborative propagation. Then the model uses the alternate learning method to optimize the model prediction unit and the knowledge graph embedding unit at the same time, and calculate the user’s feature vector through the interaction unit. Finally, the user feature vector and the item feature vector are sent to the prediction link and the interaction probability between the user and the item is calculated through the inner product operation and normalization of the vector. Comparative experiments are conducted on three public datasets with seven baseline models. On the MovieLens-1M dataset, AUC is increased by 0.25% to 37.41%, and ACC is increased by 0.78% to 49.44%; on the Book-Crossing dataset, AUC is increased by 0.04% to 19.38%, and ACC is increased by 6.49% to 18.60%; on the Last.FM dataset, AUC is increased by 1.33% to 33.50%, and ACC is increased by 0.36% to 30.66%. Experimental results show that the model proposed has improved performance compared with other benchmark models. |
| first_indexed | 2024-04-12T13:41:39Z |
| format | Article |
| id | doaj.art-da295c51d9494b83b73c7d935e946c06 |
| institution | Directory Open Access Journal |
| issn | 1673-9418 |
| language | zho |
| last_indexed | 2024-04-12T13:41:39Z |
| publishDate | 2022-06-01 |
| publisher | Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press |
| record_format | Article |
| series | Jisuanji kexue yu tansuo |
| spelling | doaj.art-da295c51d9494b83b73c7d935e946c062022-12-22T03:30:50ZzhoJournal of Computer Engineering and Applications Beijing Co., Ltd., Science PressJisuanji kexue yu tansuo1673-94182022-06-011661343135310.3778/j.issn.1673-9418.2110057Hybrid Recommendation Model of Knowledge Graph and Graph Convolutional NetworkGUO Xiaowang, XIA Hongbin, LIU Yuan01. School of Artificial Intelligence and Computer, Jiangnan University, Wuxi, Jiangsu 214122, China;2. Jiangsu Key Laboratory of Media Design and Software Technology, Wuxi, Jiangsu 214122, ChinaConcerning the failure of most current recommendation models based on knowledge graph to adequately model users’ characteristics, the neighborhood relationship between entities in the knowledge graph is not considered. This paper proposes a hybrid recommendation model that combines knowledge graph and graph convolutional network (HKC). Firstly, the KGCN (knowledge graph convolutional networks for recommender systems) algorithm is used to capture the correlation between items, and obtain the feature vector of the item through neighborhood aggregation unit. The entities associated with the user in the knowledge graph are extracted through collaborative propagation. Then the model uses the alternate learning method to optimize the model prediction unit and the knowledge graph embedding unit at the same time, and calculate the user’s feature vector through the interaction unit. Finally, the user feature vector and the item feature vector are sent to the prediction link and the interaction probability between the user and the item is calculated through the inner product operation and normalization of the vector. Comparative experiments are conducted on three public datasets with seven baseline models. On the MovieLens-1M dataset, AUC is increased by 0.25% to 37.41%, and ACC is increased by 0.78% to 49.44%; on the Book-Crossing dataset, AUC is increased by 0.04% to 19.38%, and ACC is increased by 6.49% to 18.60%; on the Last.FM dataset, AUC is increased by 1.33% to 33.50%, and ACC is increased by 0.36% to 30.66%. Experimental results show that the model proposed has improved performance compared with other benchmark models.http://fcst.ceaj.org/fileup/1673-9418/PDF/2110057.pdf|recommender system|knowledge graph|alternate learning|neighborhood aggregation |
| spellingShingle | GUO Xiaowang, XIA Hongbin, LIU Yuan Hybrid Recommendation Model of Knowledge Graph and Graph Convolutional Network Jisuanji kexue yu tansuo |recommender system|knowledge graph|alternate learning|neighborhood aggregation |
| title | Hybrid Recommendation Model of Knowledge Graph and Graph Convolutional Network |
| title_full | Hybrid Recommendation Model of Knowledge Graph and Graph Convolutional Network |
| title_fullStr | Hybrid Recommendation Model of Knowledge Graph and Graph Convolutional Network |
| title_full_unstemmed | Hybrid Recommendation Model of Knowledge Graph and Graph Convolutional Network |
| title_short | Hybrid Recommendation Model of Knowledge Graph and Graph Convolutional Network |
| title_sort | hybrid recommendation model of knowledge graph and graph convolutional network |
| topic | |recommender system|knowledge graph|alternate learning|neighborhood aggregation |
| url | http://fcst.ceaj.org/fileup/1673-9418/PDF/2110057.pdf |
| work_keys_str_mv | AT guoxiaowangxiahongbinliuyuan hybridrecommendationmodelofknowledgegraphandgraphconvolutionalnetwork |