Agricultural Product Recommendation Model based on BMF

• Autores: Fusheng Wang, Dengyun Zhu, Xiangzhen He, Qi Guo, Dongjiao Zhang, Zhenyang Ren, Yuxian Du
• Localización: Applied Mathematics and Nonlinear Sciences, ISSN-e 2444-8656, Vol. 5, Nº. 2, 2020, págs. 415-424
• Idioma: inglés
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• Resumen

  • 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.