Agricultural Product Recommendation Model based on BMF
In this article, based on the collaborative deep learning (CDL) and convolutional matrix factorisation (ConvMF), the language model BERT is used to replace the traditional word vector construction method, and the bidirectional long–short time memory network Bi-LSTM is used to construct an improved c...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Sciendo
2020-12-01
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| Series: | Applied Mathematics and Nonlinear Sciences |
| Subjects: | |
| Online Access: | https://doi.org/10.2478/amns.2020.2.00060 |
| _version_ | 1818745171496402944 |
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| author | Wan Fucheng Zhu Dengyun He Xiangzhen Guo Qi Zhang Dongjiao Ren Zhenyang Du Yuxiang |
| author_facet | Wan Fucheng Zhu Dengyun He Xiangzhen Guo Qi Zhang Dongjiao Ren Zhenyang Du Yuxiang |
| author_sort | Wan Fucheng |
| collection | DOAJ |
| description | In this article, based on the collaborative deep learning (CDL) and convolutional matrix factorisation (ConvMF), the language model BERT is used to replace the traditional word vector construction method, and the bidirectional long–short time memory network Bi-LSTM is used to construct an improved collaborative filtering model BMF, which not only solves the phenomenon of ‘polysemy’, but also alleviates the problem of sparse scoring matrix data. Experiments show that the proposed model is effective and superior to CDL and ConvMF. The trained MSE value is 1.031, which is 9.7% lower than ConvMF. |
| first_indexed | 2024-12-18T02:55:58Z |
| format | Article |
| id | doaj.art-fcb0a5a3bd8649b0a4780dc9b66e92ae |
| institution | Directory Open Access Journal |
| issn | 2444-8656 |
| language | English |
| last_indexed | 2024-12-18T02:55:58Z |
| publishDate | 2020-12-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Applied Mathematics and Nonlinear Sciences |
| spelling | doaj.art-fcb0a5a3bd8649b0a4780dc9b66e92ae2022-12-21T21:23:22ZengSciendoApplied Mathematics and Nonlinear Sciences2444-86562020-12-015241542410.2478/amns.2020.2.00060Agricultural Product Recommendation Model based on BMFWan Fucheng0Zhu Dengyun1He Xiangzhen2Guo Qi3Zhang Dongjiao4Ren Zhenyang5Du Yuxiang6Key Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education Northwest, Minzu University, Lanzhou, ChinaNorthwest Minzu University, Lanzhou, ChinaKey Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education Northwest, Minzu University, Lanzhou, ChinaKey Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education Northwest, Minzu University, Lanzhou, ChinaKey Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education Northwest, Minzu University, Lanzhou, ChinaKey Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education Northwest, Minzu University, Lanzhou, ChinaDeakin University, Geelong, AustraliaIn this article, based on the collaborative deep learning (CDL) and convolutional matrix factorisation (ConvMF), the language model BERT is used to replace the traditional word vector construction method, and the bidirectional long–short time memory network Bi-LSTM is used to construct an improved collaborative filtering model BMF, which not only solves the phenomenon of ‘polysemy’, but also alleviates the problem of sparse scoring matrix data. Experiments show that the proposed model is effective and superior to CDL and ConvMF. The trained MSE value is 1.031, which is 9.7% lower than ConvMF.https://doi.org/10.2478/amns.2020.2.00060recommendation systembert modelbi-lstm coste4395 |
| spellingShingle | Wan Fucheng Zhu Dengyun He Xiangzhen Guo Qi Zhang Dongjiao Ren Zhenyang Du Yuxiang Agricultural Product Recommendation Model based on BMF Applied Mathematics and Nonlinear Sciences recommendation system bert model bi-lstm cost e4395 |
| title | Agricultural Product Recommendation Model based on BMF |
| title_full | Agricultural Product Recommendation Model based on BMF |
| title_fullStr | Agricultural Product Recommendation Model based on BMF |
| title_full_unstemmed | Agricultural Product Recommendation Model based on BMF |
| title_short | Agricultural Product Recommendation Model based on BMF |
| title_sort | agricultural product recommendation model based on bmf |
| topic | recommendation system bert model bi-lstm cost e4395 |
| url | https://doi.org/10.2478/amns.2020.2.00060 |
| work_keys_str_mv | AT wanfucheng agriculturalproductrecommendationmodelbasedonbmf AT zhudengyun agriculturalproductrecommendationmodelbasedonbmf AT hexiangzhen agriculturalproductrecommendationmodelbasedonbmf AT guoqi agriculturalproductrecommendationmodelbasedonbmf AT zhangdongjiao agriculturalproductrecommendationmodelbasedonbmf AT renzhenyang agriculturalproductrecommendationmodelbasedonbmf AT duyuxiang agriculturalproductrecommendationmodelbasedonbmf |