A Recommendation Algorithm Based on Restricted Boltzmann Machine
In the case where the amount of data is too large, the recommended results output by the RBM model will be broader Besides, many collaborative filtering algorithms currently do not handle large data sets better So, we try to use the deep learning technology to strengthen the personalized recommendat...
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| Format: | Article |
| Language: | zho |
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Harbin University of Science and Technology Publications
2020-10-01
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| Series: | Journal of Harbin University of Science and Technology |
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| Online Access: | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=1870 |
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| _version_ | 1849398120607121408 |
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| author | WANG Weibing ZHANG Lichao XU Qian |
| author_facet | WANG Weibing ZHANG Lichao XU Qian |
| author_sort | WANG Weibing |
| collection | DOAJ |
| description | In the case where the amount of data is too large, the recommended results output by the RBM model will be broader Besides, many collaborative filtering algorithms currently do not handle large data sets better So, we try to use the deep learning technology to strengthen the personalized recommendation model We propose a hybrid recommendation model combining the bound Boltzmann model and the hidden factor model First, we use the RBM algorithm to generate candidate sets, and score the sparse matrix of the candidate set Then we use the LFM model to sort the candidate results and select the optimal solution for recommendation The hybrid model is validated using used large public datasets It can be seen from the verification that compared with the traditional recommendation model, the proposed method can improve the accuracy of the score prediction |
| format | Article |
| id | doaj-art-a3c858483d4e4efdbeaeb5709e4e5b9f |
| institution | Kabale University |
| issn | 1007-2683 |
| language | zho |
| publishDate | 2020-10-01 |
| publisher | Harbin University of Science and Technology Publications |
| record_format | Article |
| series | Journal of Harbin University of Science and Technology |
| spelling | doaj-art-a3c858483d4e4efdbeaeb5709e4e5b9f2025-08-20T03:38:43ZzhoHarbin University of Science and Technology PublicationsJournal of Harbin University of Science and Technology1007-26832020-10-012505626810.15938/j.jhust.2020.05.009A Recommendation Algorithm Based on Restricted Boltzmann MachineWANG Weibing0ZHANG Lichao1XU Qian2School of Computer Science and Technology， Harbin University of Science and Technology, Harbin 150080, ChinaSchool of Computer Science and Technology， Harbin University of Science and Technology, Harbin 150080, ChinaHarbin Branch of Heilongjiang Power corporation, Harbin 150080, ChinaIn the case where the amount of data is too large, the recommended results output by the RBM model will be broader Besides, many collaborative filtering algorithms currently do not handle large data sets better So, we try to use the deep learning technology to strengthen the personalized recommendation model We propose a hybrid recommendation model combining the bound Boltzmann model and the hidden factor model First, we use the RBM algorithm to generate candidate sets, and score the sparse matrix of the candidate set Then we use the LFM model to sort the candidate results and select the optimal solution for recommendation The hybrid model is validated using used large public datasets It can be seen from the verification that compared with the traditional recommendation model, the proposed method can improve the accuracy of the score predictionhttps://hlgxb.hrbust.edu.cn/#/digest?ArticleID=1870recommendation algorithmdeep learningrestricted boltzmann machinelatent factor model |
| spellingShingle | WANG Weibing ZHANG Lichao XU Qian A Recommendation Algorithm Based on Restricted Boltzmann Machine Journal of Harbin University of Science and Technology recommendation algorithm deep learning restricted boltzmann machine latent factor model |
| title | A Recommendation Algorithm Based on Restricted Boltzmann Machine |
| title_full | A Recommendation Algorithm Based on Restricted Boltzmann Machine |
| title_fullStr | A Recommendation Algorithm Based on Restricted Boltzmann Machine |
| title_full_unstemmed | A Recommendation Algorithm Based on Restricted Boltzmann Machine |
| title_short | A Recommendation Algorithm Based on Restricted Boltzmann Machine |
| title_sort | recommendation algorithm based on restricted boltzmann machine |
| topic | recommendation algorithm deep learning restricted boltzmann machine latent factor model |
| url | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=1870 |
| work_keys_str_mv | AT wangweibing arecommendationalgorithmbasedonrestrictedboltzmannmachine AT zhanglichao arecommendationalgorithmbasedonrestrictedboltzmannmachine AT xuqian arecommendationalgorithmbasedonrestrictedboltzmannmachine AT wangweibing recommendationalgorithmbasedonrestrictedboltzmannmachine AT zhanglichao recommendationalgorithmbasedonrestrictedboltzmannmachine AT xuqian recommendationalgorithmbasedonrestrictedboltzmannmachine |