Personalized recommendation system based on social tags in the era of Internet of Things
With the rapid development of the Internet, recommendation systems have received widespread attention as an effective way to solve information overload. Social tagging technology can both reflect users’ interests and describe the characteristics of the items themselves, making group recommendation t...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
De Gruyter
2022-06-01
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| Series: | Journal of Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/jisys-2022-0053 |
| _version_ | 1797998384972300288 |
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| author | Dong Jie Li Gui Ma Wenkai Liu Jianshun |
| author_facet | Dong Jie Li Gui Ma Wenkai Liu Jianshun |
| author_sort | Dong Jie |
| collection | DOAJ |
| description | With the rapid development of the Internet, recommendation systems have received widespread attention as an effective way to solve information overload. Social tagging technology can both reflect users’ interests and describe the characteristics of the items themselves, making group recommendation thus becoming a recommendation technology in urgent demand nowadays. In traditional tag-based recommendation systems, the general processing method is to calculate the similarity and then rank the recommended items according to the similarity. Without considering the influence of continuous user behavior, in this article, we propose a personalized recommendation algorithm based on social tags by combining the ideas of Markov chain and collaborative filtering. This algorithm splits the three-dimensional relationship of <user-tag-item> into two two-dimensional relationships of <user-tag> and <tag-item>. The user’s interest degree to the tags is calculated by the Markov chain model, and then the items corresponding to them are matched by the recommended tag set. The influence between tags is used to model the satisfaction of items based on the correlation between the tags contained in the matched items, and collaborative filtering is used to complete the sparse values when calculating the interest and satisfaction between user–tags and user–items to improve the accuracy of recommendations. The experiments show that in the publicly available dataset, the personalized recommendation algorithm proposed in this article has significantly improved in accuracy and recall rate compared with the existing algorithms. |
| first_indexed | 2024-04-11T10:47:56Z |
| format | Article |
| id | doaj.art-addb53f32a6f444aaa87534a35c49aea |
| institution | Directory Open Access Journal |
| issn | 2191-026X |
| language | English |
| last_indexed | 2024-04-11T10:47:56Z |
| publishDate | 2022-06-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Journal of Intelligent Systems |
| spelling | doaj.art-addb53f32a6f444aaa87534a35c49aea2022-12-22T04:29:00ZengDe GruyterJournal of Intelligent Systems2191-026X2022-06-0131168168910.1515/jisys-2022-0053Personalized recommendation system based on social tags in the era of Internet of ThingsDong Jie0Li Gui1Ma Wenkai2Liu Jianshun3Information and Control Engineering School, Shenyang Jianzhu University, Shenyang 110168, Liaoning, ChinaInformation and Control Engineering School, Shenyang Jianzhu University, Shenyang 110168, Liaoning, ChinaInformation and Control Engineering School, Shenyang Jianzhu University, Shenyang 110168, Liaoning, ChinaInformation and Control Engineering School, Shenyang Jianzhu University, Shenyang 110168, Liaoning, ChinaWith the rapid development of the Internet, recommendation systems have received widespread attention as an effective way to solve information overload. Social tagging technology can both reflect users’ interests and describe the characteristics of the items themselves, making group recommendation thus becoming a recommendation technology in urgent demand nowadays. In traditional tag-based recommendation systems, the general processing method is to calculate the similarity and then rank the recommended items according to the similarity. Without considering the influence of continuous user behavior, in this article, we propose a personalized recommendation algorithm based on social tags by combining the ideas of Markov chain and collaborative filtering. This algorithm splits the three-dimensional relationship of <user-tag-item> into two two-dimensional relationships of <user-tag> and <tag-item>. The user’s interest degree to the tags is calculated by the Markov chain model, and then the items corresponding to them are matched by the recommended tag set. The influence between tags is used to model the satisfaction of items based on the correlation between the tags contained in the matched items, and collaborative filtering is used to complete the sparse values when calculating the interest and satisfaction between user–tags and user–items to improve the accuracy of recommendations. The experiments show that in the publicly available dataset, the personalized recommendation algorithm proposed in this article has significantly improved in accuracy and recall rate compared with the existing algorithms.https://doi.org/10.1515/jisys-2022-0053social tagmarkov chainsatisfaction modelcollaborative filteringpersonalized recommendation |
| spellingShingle | Dong Jie Li Gui Ma Wenkai Liu Jianshun Personalized recommendation system based on social tags in the era of Internet of Things Journal of Intelligent Systems social tag markov chain satisfaction model collaborative filtering personalized recommendation |
| title | Personalized recommendation system based on social tags in the era of Internet of Things |
| title_full | Personalized recommendation system based on social tags in the era of Internet of Things |
| title_fullStr | Personalized recommendation system based on social tags in the era of Internet of Things |
| title_full_unstemmed | Personalized recommendation system based on social tags in the era of Internet of Things |
| title_short | Personalized recommendation system based on social tags in the era of Internet of Things |
| title_sort | personalized recommendation system based on social tags in the era of internet of things |
| topic | social tag markov chain satisfaction model collaborative filtering personalized recommendation |
| url | https://doi.org/10.1515/jisys-2022-0053 |
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