Article
Agricultural Product Recommendation Model based on BMF
Authors
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Abstract
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.
Keywords
Recommendation system, BERT model, Bi-LSTM cost, E4395
Citation
Wan, F., Zhu, D., He, X., Guo, Q., Zhang, D., Ren, Z., & Du, Y. (2020). Agricultural product recommendation model based on BMF. Applied Mathematics and Nonlinear Sciences, 5(2), 415–424. https://doi.org/10.2478/amns.2020.2.00060
F. Wan, D. Zhu, X. He, Q. Guo, D. Zhang, Z. Ren and Y. Du, “Agricultural product recommendation model based on BMF,” Applied Mathematics and Nonlinear Sciences, vol. 5, no. 2, pp. 415–424, 2020, doi: 10.2478/amns.2020.2.00060.
Wan F, Zhu D, He X, Guo Q, Zhang D, Ren Z, Du Y. Agricultural product recommendation model based on BMF. Applied Mathematics and Nonlinear Sciences. 2020;5(2):415–424. doi:10.2478/amns.2020.2.00060.
Wan, F., Zhu, D., He, X., Guo, Q., Zhang, D., Ren, Z. and Du, Y. (2020), ‘Agricultural product recommendation model based on BMF’, Applied Mathematics and Nonlinear Sciences, 5(2), pp. 415–424. Available at: https://doi.org/10.2478/amns.2020.2.00060.
Wan, Fucheng, et al. “Agricultural Product Recommendation Model Based on BMF.” Applied Mathematics and Nonlinear Sciences, vol. 5, no. 2, 2020, pp. 415–424. https://doi.org/10.2478/amns.2020.2.00060.
Wan, Fucheng, Dengyun Zhu, Xiangzhen He, Qi Guo, Dongjiao Zhang, Zhenyang Ren, and Yuxiang Du. “Agricultural Product Recommendation Model Based on BMF.” Applied Mathematics and Nonlinear Sciences 5, no. 2 (2020): 415–424. https://doi.org/10.2478/amns.2020.2.00060.
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Published by: Engineering Journals
