<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></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.

Agricultural Product Recommendation Model based on BMF

Key Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education Northwest

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"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...

Agricultural Product Recommendation Model based on BMF

Online veröffentlicht: 21. Dez. 2020

Seitenbereich: 415 - 424

Eingereicht: 10. Juli 2020

Akzeptiert: 14. Aug. 2020

DOI: https://doi.org/10.2478/amns.2020.2.00060

Schlüsselwörter
Recommendation system, BERT model, Bi-LSTM cost

© 2020 Fucheng Wan et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Agricultural Product Recommendation Model based on BMF

Online veröffentlicht: 21. Dez. 2020

Seitenbereich: 415 - 424

Eingereicht: 10. Juli 2020

Akzeptiert: 14. Aug. 2020

DOI: https://doi.org/10.2478/amns.2020.2.00060

SchlüsselwörterRecommendation system, BERT model, Bi-LSTM cost

© 2020 Fucheng Wan et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Recommendation system, BERT model, Bi-LSTM cost