Collaborative Filtering Recommendation Algorithm of Behavior Route Based on Knowledge Graph
For personalized recommendation,common recommendation algorithms include content recommendation,Item CF and User CF.However,most of these algorithms and their improved algorithms tend to focus on users' explicit feedback (tags,ra-tings,etc.) or rating data,and lack the use of multi-dimensional...
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| Format: | Article |
| Language: | zho |
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Editorial office of Computer Science
2021-11-01
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| Series: | Jisuanji kexue |
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| Online Access: | https://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2021-11-176.pdf |
| _version_ | 1818555894164619264 |
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| author | CHEN Yuan-yi, FENG Wen-long, HUANG Meng-xing, FENG Si-ling |
| author_facet | CHEN Yuan-yi, FENG Wen-long, HUANG Meng-xing, FENG Si-ling |
| author_sort | CHEN Yuan-yi, FENG Wen-long, HUANG Meng-xing, FENG Si-ling |
| collection | DOAJ |
| description | For personalized recommendation,common recommendation algorithms include content recommendation,Item CF and User CF.However,most of these algorithms and their improved algorithms tend to focus on users' explicit feedback (tags,ra-tings,etc.) or rating data,and lack the use of multi-dimensional user behavior and behavior order,resulting in low recommendation accuracy and cold start problems.In order to improve the recommendation accuracy,a collaborative filtering recommendation algorithm based on knowledge graph (BR-CF) is proposed.Firstly,according to the user behavior data,behavior graph and behavior route are created considering the behavior order,and then the vectorization technology (Keras Tokenizer) is used.Finally,the similarity between multi-dimensional behavior route vectors is calculated,and the route collaborative filtering recommendation is carried out for each dimension.On this basis,two improved algorithms combining BR-CF and Item CF are proposed.The expe-rimental results show that the BR-CF algorithm can recommend effectively in multiple dimensions on the user behavior dataset of Ali Tianchi,realize the full utilization of data and the diversity of recommendation,and the improved algorithm can improve the recommendation performance of Item CF. |
| first_indexed | 2024-12-13T01:59:04Z |
| format | Article |
| id | doaj.art-5157c0f6ca2a486dbcc01a9a5ebec0f8 |
| institution | Directory Open Access Journal |
| issn | 1002-137X |
| language | zho |
| last_indexed | 2024-12-13T01:59:04Z |
| publishDate | 2021-11-01 |
| publisher | Editorial office of Computer Science |
| record_format | Article |
| series | Jisuanji kexue |
| spelling | doaj.art-5157c0f6ca2a486dbcc01a9a5ebec0f82022-12-22T00:03:19ZzhoEditorial office of Computer ScienceJisuanji kexue1002-137X2021-11-01481117618310.11896/jsjkx.201000004Collaborative Filtering Recommendation Algorithm of Behavior Route Based on Knowledge GraphCHEN Yuan-yi, FENG Wen-long, HUANG Meng-xing, FENG Si-ling01 College of Computer Science and Cyberspace Security,Hainan University,Haikou 570228,China<br/>2 College of Information Science Technology,Hainan University,Haikou 570228,China<br/>3 State Key Laboratory of Marine Resource Utilization in South China Sea,Hainan University,Haikou 570228,ChinaFor personalized recommendation,common recommendation algorithms include content recommendation,Item CF and User CF.However,most of these algorithms and their improved algorithms tend to focus on users' explicit feedback (tags,ra-tings,etc.) or rating data,and lack the use of multi-dimensional user behavior and behavior order,resulting in low recommendation accuracy and cold start problems.In order to improve the recommendation accuracy,a collaborative filtering recommendation algorithm based on knowledge graph (BR-CF) is proposed.Firstly,according to the user behavior data,behavior graph and behavior route are created considering the behavior order,and then the vectorization technology (Keras Tokenizer) is used.Finally,the similarity between multi-dimensional behavior route vectors is calculated,and the route collaborative filtering recommendation is carried out for each dimension.On this basis,two improved algorithms combining BR-CF and Item CF are proposed.The expe-rimental results show that the BR-CF algorithm can recommend effectively in multiple dimensions on the user behavior dataset of Ali Tianchi,realize the full utilization of data and the diversity of recommendation,and the improved algorithm can improve the recommendation performance of Item CF.https://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2021-11-176.pdfrecommendation algorithm|behavior order|behavior graph|behavior route|route coordination|multi-dimensional recommendation |
| spellingShingle | CHEN Yuan-yi, FENG Wen-long, HUANG Meng-xing, FENG Si-ling Collaborative Filtering Recommendation Algorithm of Behavior Route Based on Knowledge Graph Jisuanji kexue recommendation algorithm|behavior order|behavior graph|behavior route|route coordination|multi-dimensional recommendation |
| title | Collaborative Filtering Recommendation Algorithm of Behavior Route Based on Knowledge Graph |
| title_full | Collaborative Filtering Recommendation Algorithm of Behavior Route Based on Knowledge Graph |
| title_fullStr | Collaborative Filtering Recommendation Algorithm of Behavior Route Based on Knowledge Graph |
| title_full_unstemmed | Collaborative Filtering Recommendation Algorithm of Behavior Route Based on Knowledge Graph |
| title_short | Collaborative Filtering Recommendation Algorithm of Behavior Route Based on Knowledge Graph |
| title_sort | collaborative filtering recommendation algorithm of behavior route based on knowledge graph |
| topic | recommendation algorithm|behavior order|behavior graph|behavior route|route coordination|multi-dimensional recommendation |
| url | https://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2021-11-176.pdf |
| work_keys_str_mv | AT chenyuanyifengwenlonghuangmengxingfengsiling collaborativefilteringrecommendationalgorithmofbehaviorroutebasedonknowledgegraph |