Two-Terminal Neighbor Information Fusion Recommendation Algorithm Based on Knowledge Graph
In view of the problem that some recommendation algorithms based on knowledge graphs only aggregate one end of the neighbors and cannot effectively determine the relationship between entities and users, this paper proposes a dual-end neighbor aggregation recommendation algorithm based on knowledge g...
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| Format: | Article |
| Language: | zho |
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Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press
2022-06-01
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| Series: | Jisuanji kexue yu tansuo |
| Subjects: | |
| Online Access: | http://fcst.ceaj.org/fileup/1673-9418/PDF/2012054.pdf |
| _version_ | 1811241757559488512 |
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| author | WANG Baoliang, PAN Wencai |
| author_facet | WANG Baoliang, PAN Wencai |
| author_sort | WANG Baoliang, PAN Wencai |
| collection | DOAJ |
| description | In view of the problem that some recommendation algorithms based on knowledge graphs only aggregate one end of the neighbors and cannot effectively determine the relationship between entities and users, this paper proposes a dual-end neighbor aggregation recommendation algorithm based on knowledge graphs. This algorithm explores the internal connections of knowledge graphs to discover the potential relationship between users and items. On the user side, this paper proposes a method of aggregating user neighbor information. In the relational space of the knowledge graph, the knowledge graph is used to spread and extract the user’s potential interest, and iteratively inject the potential interest into the user characteristics with attention bias to generate user embedding representation vector. At the item side, the user vector that aggregates the user’s neighbor information is sent to the KGCN (knowledge graph convolutional networks) model, and when the polymer product and its neighbor infor-mation are used, a new aggregation method is used to generate the item embedding representation. Finally, the obtained vector is sent to train. Through the inner product operation of the vector and normalization, the association score between the user and the item is obtained. Then the training is carried out in the training set to optimize the parameters. Comparative experiments are conducted on two public datasets. Compared with the baseline, on the Book-Crossing dataset, AUC and ACC are increased by 1.72% and 4.24%, and on the Last.FM dataset, AUC and ACC are increased by 1.07% and 1.14%. It is proven that the effectiveness of the algorithm is improved after the information of neighbors at both ends is aggregated. |
| first_indexed | 2024-04-12T13:40:57Z |
| format | Article |
| id | doaj.art-3024d2246b77474eaeca3b9ac4704ccc |
| institution | Directory Open Access Journal |
| issn | 1673-9418 |
| language | zho |
| last_indexed | 2024-04-12T13:40:57Z |
| 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-3024d2246b77474eaeca3b9ac4704ccc2022-12-22T03:30:50ZzhoJournal of Computer Engineering and Applications Beijing Co., Ltd., Science PressJisuanji kexue yu tansuo1673-94182022-06-011661354136110.3778/j.issn.1673-9418.2012054Two-Terminal Neighbor Information Fusion Recommendation Algorithm Based on Knowledge GraphWANG Baoliang, PAN Wencai01. Information and Network Center, Tianjin University, Tianjin 300072, China;2. School of Electrical Automation and Information Engineering, Tianjin University, Tianjin 300072, ChinaIn view of the problem that some recommendation algorithms based on knowledge graphs only aggregate one end of the neighbors and cannot effectively determine the relationship between entities and users, this paper proposes a dual-end neighbor aggregation recommendation algorithm based on knowledge graphs. This algorithm explores the internal connections of knowledge graphs to discover the potential relationship between users and items. On the user side, this paper proposes a method of aggregating user neighbor information. In the relational space of the knowledge graph, the knowledge graph is used to spread and extract the user’s potential interest, and iteratively inject the potential interest into the user characteristics with attention bias to generate user embedding representation vector. At the item side, the user vector that aggregates the user’s neighbor information is sent to the KGCN (knowledge graph convolutional networks) model, and when the polymer product and its neighbor infor-mation are used, a new aggregation method is used to generate the item embedding representation. Finally, the obtained vector is sent to train. Through the inner product operation of the vector and normalization, the association score between the user and the item is obtained. Then the training is carried out in the training set to optimize the parameters. Comparative experiments are conducted on two public datasets. Compared with the baseline, on the Book-Crossing dataset, AUC and ACC are increased by 1.72% and 4.24%, and on the Last.FM dataset, AUC and ACC are increased by 1.07% and 1.14%. It is proven that the effectiveness of the algorithm is improved after the information of neighbors at both ends is aggregated.http://fcst.ceaj.org/fileup/1673-9418/PDF/2012054.pdf|recommendation algorithm|knowledge graph|neighbor aggregation |
| spellingShingle | WANG Baoliang, PAN Wencai Two-Terminal Neighbor Information Fusion Recommendation Algorithm Based on Knowledge Graph Jisuanji kexue yu tansuo |recommendation algorithm|knowledge graph|neighbor aggregation |
| title | Two-Terminal Neighbor Information Fusion Recommendation Algorithm Based on Knowledge Graph |
| title_full | Two-Terminal Neighbor Information Fusion Recommendation Algorithm Based on Knowledge Graph |
| title_fullStr | Two-Terminal Neighbor Information Fusion Recommendation Algorithm Based on Knowledge Graph |
| title_full_unstemmed | Two-Terminal Neighbor Information Fusion Recommendation Algorithm Based on Knowledge Graph |
| title_short | Two-Terminal Neighbor Information Fusion Recommendation Algorithm Based on Knowledge Graph |
| title_sort | two terminal neighbor information fusion recommendation algorithm based on knowledge graph |
| topic | |recommendation algorithm|knowledge graph|neighbor aggregation |
| url | http://fcst.ceaj.org/fileup/1673-9418/PDF/2012054.pdf |
| work_keys_str_mv | AT wangbaoliangpanwencai twoterminalneighborinformationfusionrecommendationalgorithmbasedonknowledgegraph |