Collaborative filtering recommendation algorithm based on hybrid clustering and user preferences fusion
Problems such as low recommendation efficiency and recommendation quality to be improved generally exist in the traditional collaborative filtering recommendation algorithm. In order to improve and solve these problems, the collaborative filtering recommendation algorithm integrates mixed clustering...
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| Format: | Article |
| Language: | zho |
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National Computer System Engineering Research Institute of China
2022-04-01
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| Series: | Dianzi Jishu Yingyong |
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| Online Access: | http://www.chinaaet.com/article/3000148313 |
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| _version_ | 1850082649643679744 |
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| author | Ma Tian Yu Benguo Zhang Jing Song Wenai Jing Yu |
| author_facet | Ma Tian Yu Benguo Zhang Jing Song Wenai Jing Yu |
| author_sort | Ma Tian |
| collection | DOAJ |
| description | Problems such as low recommendation efficiency and recommendation quality to be improved generally exist in the traditional collaborative filtering recommendation algorithm. In order to improve and solve these problems, the collaborative filtering recommendation algorithm integrates mixed clustering with user interests and preferences, and the recommendation quality has been significantly improved after verification. Firstly, a multiple mixed clustering model of Canopy+ Bi-Kmeans was constructed according to the personal information of users. The proposed mixed clustering model was used to divide all users into multiple clusters, and the interest preferences of each user were fused into the generated clusters to form a new similarity calculation model. Secondly, the weight classification method based on TF-IDF algorithm is used to calculate the weight of users on labels, and the exponential decay function incorporating time coefficient is used to capture the change of users′ interest preference with time. Finally, weighted fusion is used to combine user preferences with mixed clustering model to match more similar neighbor users, calculate project scores and make recommendations. The experimental results show that the proposed method can improve the recommendation quality and reliability. |
| format | Article |
| id | doaj-art-852b56670af64d31a2d76d83a9bf648f |
| institution | DOAJ |
| issn | 0258-7998 |
| language | zho |
| publishDate | 2022-04-01 |
| publisher | National Computer System Engineering Research Institute of China |
| record_format | Article |
| series | Dianzi Jishu Yingyong |
| spelling | doaj-art-852b56670af64d31a2d76d83a9bf648f2025-08-20T02:44:28ZzhoNational Computer System Engineering Research Institute of ChinaDianzi Jishu Yingyong0258-79982022-04-01484293310.16157/j.issn.0258-7998.2120863000148313Collaborative filtering recommendation algorithm based on hybrid clustering and user preferences fusionMa Tian0Yu Benguo1Zhang Jing2Song Wenai3Jing Yu4Software School，North University of China，Taiyuan 030051，ChinaSchool of Biomedical Information and Engineering，Hainan Medical University，Haikou 571199，ChinaSoftware School，North University of China，Taiyuan 030051，ChinaSoftware School，North University of China，Taiyuan 030051，ChinaSoftware School，North University of China，Taiyuan 030051，ChinaProblems such as low recommendation efficiency and recommendation quality to be improved generally exist in the traditional collaborative filtering recommendation algorithm. In order to improve and solve these problems, the collaborative filtering recommendation algorithm integrates mixed clustering with user interests and preferences, and the recommendation quality has been significantly improved after verification. Firstly, a multiple mixed clustering model of Canopy+ Bi-Kmeans was constructed according to the personal information of users. The proposed mixed clustering model was used to divide all users into multiple clusters, and the interest preferences of each user were fused into the generated clusters to form a new similarity calculation model. Secondly, the weight classification method based on TF-IDF algorithm is used to calculate the weight of users on labels, and the exponential decay function incorporating time coefficient is used to capture the change of users′ interest preference with time. Finally, weighted fusion is used to combine user preferences with mixed clustering model to match more similar neighbor users, calculate project scores and make recommendations. The experimental results show that the proposed method can improve the recommendation quality and reliability.http://www.chinaaet.com/article/3000148313recommendation algorithmweight labeltime attenuation coefficientexponential decay functionhybrid clustering |
| spellingShingle | Ma Tian Yu Benguo Zhang Jing Song Wenai Jing Yu Collaborative filtering recommendation algorithm based on hybrid clustering and user preferences fusion Dianzi Jishu Yingyong recommendation algorithm weight label time attenuation coefficient exponential decay function hybrid clustering |
| title | Collaborative filtering recommendation algorithm based on hybrid clustering and user preferences fusion |
| title_full | Collaborative filtering recommendation algorithm based on hybrid clustering and user preferences fusion |
| title_fullStr | Collaborative filtering recommendation algorithm based on hybrid clustering and user preferences fusion |
| title_full_unstemmed | Collaborative filtering recommendation algorithm based on hybrid clustering and user preferences fusion |
| title_short | Collaborative filtering recommendation algorithm based on hybrid clustering and user preferences fusion |
| title_sort | collaborative filtering recommendation algorithm based on hybrid clustering and user preferences fusion |
| topic | recommendation algorithm weight label time attenuation coefficient exponential decay function hybrid clustering |
| url | http://www.chinaaet.com/article/3000148313 |
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